Research Projects Directory

Research Projects Directory

11,354 active projects

This information was updated 5/28/2024

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. Views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program. Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

bime591-gene

understanding genetic research using AoU understanding genetic research using AoU understanding genetic research using AoU

Scientific Questions Being Studied

understanding genetic research using AoU
understanding genetic research using AoU
understanding genetic research using AoU

Project Purpose(s)

  • Educational

Scientific Approaches

understanding genetic research using AoU
understanding genetic research using AoU
understanding genetic research using AoU

Anticipated Findings

understanding genetic research using AoU
understanding genetic research using AoU
understanding genetic research using AoU

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Ya Lin Chen - Graduate Trainee, University of Washington

bime591

LDL and the genetic predisposition to low LDL. LDL and the genetic predisposition to low LDL. LDL and the genetic predisposition to low LDL.

Scientific Questions Being Studied

LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.

Project Purpose(s)

  • Educational

Scientific Approaches

LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.

Anticipated Findings

LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.
LDL and the genetic predisposition to low LDL.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Ya Lin Chen - Graduate Trainee, University of Washington

Marzban

The specific scientific question I aim to explore is how genetic factors contribute to the development of glaucoma across diverse populations. This is crucial because understanding the genetic underpinnings of glaucoma beyond European ancestry can provide comprehensive insights into its…

Scientific Questions Being Studied

The specific scientific question I aim to explore is how genetic factors contribute to the development of glaucoma across diverse populations. This is crucial because understanding the genetic underpinnings of glaucoma beyond European ancestry can provide comprehensive insights into its pathogenesis and enable tailored preventive measures and treatments for affected individuals worldwide. By exploring data on different genetic backgrounds, I hope to identify novel genetic markers associated with glaucoma susceptibility across various ethnic groups, thereby advancing our understanding of this blinding disease and informing more inclusive approaches to its management and prevention.

Project Purpose(s)

  • Ancestry

Scientific Approaches

To explore genetic contributions to glaucoma across diverse populations, I'll gather genomic data from various ethnicity affected by glaucoma. I'll use GWAS and PRS analyses to pinpoint genetic variants linked to glaucoma susceptibility in each group. Bioinformatics tools will help annotate and prioritise these variants, revealing their biological roles in glaucoma development. Comparative genomic analyses will uncover common and population-specific genetic risk factors, illuminating diverse genetic architectures. This holistic approach will deepen our understanding of glaucoma genetics, guiding personalised medicine and public health efforts.

Anticipated Findings

The anticipated findings from this study are expected to significantly contribute to the body of scientific knowledge in the field of glaucoma genetics. Firstly, by identifying novel genetic variants associated with glaucoma susceptibility across diverse populations, the study aims to expand the current understanding of the genetic architecture underlying this complex disease beyond European ancestry. These findings will enrich existing knowledge and provide insights into the genetic mechanisms driving glaucoma development in populations with different genetic backgrounds. Moreover, the comparative genomic analyses across various ethnic groups are likely to reveal both shared and population-specific genetic risk factors for glaucoma. This comparative approach will enhance our understanding of the interplay between genetic and environmental factors in glaucoma pathogenesis and may uncover potential targets for therapeutic intervention tailored to specific population subgroups.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Maryam Marzban - Research Fellow, The Council of The Queensland Institute of Medical Research

AoU data exploration for office houRs

This workspace is for a group of scientists who are currently developing their R programming skills. The notebooks we create and share here are to further our understanding of R and gain familiarity with the All of Us dataset.

Scientific Questions Being Studied

This workspace is for a group of scientists who are currently developing their R programming skills. The notebooks we create and share here are to further our understanding of R and gain familiarity with the All of Us dataset.

Project Purpose(s)

  • Educational

Scientific Approaches

This workspace does not use an scientific approach. It is intended solely for educational purposes. We will likely use most of the available datasets sets, since we're learning about the contents of the All of Us datasets and how to access it.

Anticipated Findings

We do not anticipate any findings from this workspace. By improving our understand of R and the All of Us dataset, we hope that we will be able to apply this knowledge to future projects.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Prostate_Cancer_Risk_Variant_Analysis_2024_v1_snv_chr10_11

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in…

Scientific Questions Being Studied

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in both factors. Our goals are to find ethnic specific risk factors using survey-based features and genetic risk factors using the genetic variants data.

Project Purpose(s)

  • Disease Focused Research (prostate cancer)
  • Population Health
  • Educational
  • Methods Development
  • Ancestry

Scientific Approaches

To achieve our goals we will use statistical tests and state-of-the-art tools to compare case and control genomes in order to identify variants that appear disproportionally in cases and genes with heavy variant load in cases. Such tools include the Evolutionary Action method and the software packages EMMAX and ACAT, amongst others.

Anticipated Findings

We anticipate obtaining lists of candidate genes and their variants that drive PRAD in African American men and in White American men, which we will contrast and compare with the current knowledge (e.g. BRCA1, BRCA2, and HOXB13 genes). This work may provide new genetic targets that affect the development and progression of PRAD, especially amongst the African American men and reduce the racial disparity in genetic risk diagnosis.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Prostate_Cancer_Risk_Variant_Analysis_2024_v1_snv_chr9_10

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in…

Scientific Questions Being Studied

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in both factors. Our goals are to find ethnic specific risk factors using survey-based features and genetic risk factors using the genetic variants data.

Project Purpose(s)

  • Disease Focused Research (prostate cancer)
  • Population Health
  • Educational
  • Methods Development
  • Ancestry

Scientific Approaches

To achieve our goals we will use statistical tests and state-of-the-art tools to compare case and control genomes in order to identify variants that appear disproportionally in cases and genes with heavy variant load in cases. Such tools include the Evolutionary Action method and the software packages EMMAX and ACAT, amongst others.

Anticipated Findings

We anticipate obtaining lists of candidate genes and their variants that drive PRAD in African American men and in White American men, which we will contrast and compare with the current knowledge (e.g. BRCA1, BRCA2, and HOXB13 genes). This work may provide new genetic targets that affect the development and progression of PRAD, especially amongst the African American men and reduce the racial disparity in genetic risk diagnosis.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Evaluation of patients diagnosed with neuroendocrine tumors

What causes neuroendocrine tumors (NETs) is still unknown, and no avoidable risk factors have been reported yet. Established prognostic factors for NETs include age at diagnosis, sex, race, histologic grade, and stage at diagnosis6. Pathological factors, including the immunohistochemical proliferation…

Scientific Questions Being Studied

What causes neuroendocrine tumors (NETs) is still unknown, and no avoidable risk factors have been reported yet. Established prognostic factors for NETs include age at diagnosis, sex, race, histologic grade, and stage at diagnosis6. Pathological factors, including the immunohistochemical proliferation marker Ki67, lymphatic and blood vessel invasion, and level of chromogranin A and synaptophysin expression, can also affect prognosis of NETs. This study will answer the question that what causes NETs and affects the prognosis of NETs.

Project Purpose(s)

  • Disease Focused Research (neuroendocrine tumors)

Scientific Approaches

First, EHR data linked with demographic data will be used to generate analyzable data. Second, descriptive analysis is used to describe the basic characteristics of study population, including demographic information and clinicopathological information. By applying unadjusted and adjusted models, we should report if there are associations between these characteristics and the risk of NETs. Third, we will compare the survival of patients with different characteristics by using Kaplan Meier curves and Cox proportional hazards. The final results of this research should be a finalized manuscript.

Anticipated Findings

We anticipate to validate the risk and prognostic factors of NET patients. Identification of patients at high risk for developing NETs and poor clinical outcomes of NETs is critical to improve care. The overall goal of our study is to explore novel risk and prognostic factors that could help avoid risks of developing NETs among general population and predict prognosis in patients with NETs.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Tao Xu - Graduate Trainee, University of Iowa

Prostate_Cancer_Risk_Variant_Analysis_2024_v1_snv_chr7_8

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in…

Scientific Questions Being Studied

Prostate Adenocarcinoma (PRAD) is associated with 1 in 25 African American men deaths, compared to 1 in 45 White American men deaths. Genetic and societal factors may contribute to this racial disparity and our project aims to shed light in both factors. Our goals are to find ethnic specific risk factors using survey-based features and genetic risk factors using the genetic variants data.

Project Purpose(s)

  • Disease Focused Research (prostate cancer)
  • Population Health
  • Educational
  • Methods Development
  • Ancestry

Scientific Approaches

To achieve our goals we will use statistical tests and state-of-the-art tools to compare case and control genomes in order to identify variants that appear disproportionally in cases and genes with heavy variant load in cases. Such tools include the Evolutionary Action method and the software packages EMMAX and ACAT, amongst others.

Anticipated Findings

We anticipate obtaining lists of candidate genes and their variants that drive PRAD in African American men and in White American men, which we will contrast and compare with the current knowledge (e.g. BRCA1, BRCA2, and HOXB13 genes). This work may provide new genetic targets that affect the development and progression of PRAD, especially amongst the African American men and reduce the racial disparity in genetic risk diagnosis.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of All of Us v6 GWAS on LDL Cholesterol with Regenie and dsub

The main questions this workspace is attempting to address is whether a scalable GWAS can be created on the AoU platform and whether (and how) that GWAS can be optimized for run cost and time. These questions are important for…

Scientific Questions Being Studied

The main questions this workspace is attempting to address is whether a scalable GWAS can be created on the AoU platform and whether (and how) that GWAS can be optimized for run cost and time. These questions are important for making the AoU platform—and a common genomics analysis like a GWAS—as accessible and understandable as possible for researchers of all experience levels; by attempting to address the scalability of this GWAS it will hopefully be more future-proof and allow researchers to quickly analyze future AoU data releases, and performing this GWAS multiple times and tracking cluster metrics will unveil optimized configurations to better inform future researches seeking to recreate this GWAS or perform other analyses of similar computational intensity.

Project Purpose(s)

  • Methods Development
  • Other Purpose (The purpose of this workspace is to recreate an efficient and scalable Genome Wide Association Study (GWAS) across whole genome sequenced data on an LDL Cholesterol phenotype.)

Scientific Approaches

This workspace is intended to provide a functional and scalable GWAS on AoU data. The GWAS will apply the methodologies of the hail.is GWAS tutorial, the featured workspace GWAS, Nicole DeFlaux's and Margret Sunitha's phenotype generation and PC analysis, as well as Seung Hoan Choi and Xin Wang's QC methodologies used in the GWAS demonstration project—the corresponding papers of which are available here:
https://www.biorxiv.org/content/10.1101/2022.11.29.518423v1.abstract
https://europepmc.org/article/ppr/ppr576116

Anticipated Findings

The research conducted in this study is not novel and there are no anticipated findings from this study other than a successful recreation of prior GWAS performances. The success of this replication, however, will contribute to the body of bioinformatic knowledge by further acknowledging the utility and necessity of cloud-based analysis platforms that enable genomics research. Moreover, this replication's success establishes the validity of the All of Us Researcher Workbench and dataset as usable, reliable resources with which genomic analyses can be conducted. This analysis is intended to be replicable for subsequent versions of the All of Us dataset, however the v7 BGEN files are formatted incorrectly as of June 2023 leading to issues with Regenie. This formatting issue is resolved by remaking the BGEN files with Plink at the expense of compute cost and time, but those steps are not included in this Workspace and may become unnecessary if future releases are correctly formatted.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Ya Lin Chen - Graduate Trainee, University of Washington

SGM - IBS

What is the prevalence of disorders of gut-brain interaction (DGBI), particularly irritable bowel syndrome (IBS), in patients who identify as sexual and gender minorities as compared with controls? Within this population, we also hope to analyze severity of disease, co-morbid…

Scientific Questions Being Studied

What is the prevalence of disorders of gut-brain interaction (DGBI), particularly irritable bowel syndrome (IBS), in patients who identify as sexual and gender minorities as compared with controls? Within this population, we also hope to analyze severity of disease, co-morbid conditions, socioeconomic factors, and healthcare utilization.

Project Purpose(s)

  • Disease Focused Research (Disorders of Gut Brain Interaction)

Scientific Approaches

We plan to use data from the All of Us database to identify patients who identify as sexual and gender minorities and have a diagnosis of IBS, which will be compared against a cohort of sexual and gender minorities without IBS as well as a cohort of non-sexual/gender minorities without IBS. We plan to use multivariable analysis, controlling for clinical and demographic variables, to evaluate the relationship between SGM status and functional GI disorders.

Anticipated Findings

Improved understanding of DGBI (severity of symptoms, co-morbidities) in this historically underserved population will allow us to develop more effective and personalized treatment approaches in the future. We hypothesize that SGM populations will have a higher prevalence of DGBIs as compared with controls.

Demographic Categories of Interest

  • Sex at Birth
  • Gender Identity
  • Sexual Orientation

Data Set Used

Registered Tier

Research Team

Owner:

  • Rosa Yu - Research Fellow, Boston Medical Center

environmental health_controlled Tier

The specific scientific question I intend to study is: "How does air pollution impact mental health across different populations in the United States?" This question is crucial for understanding the broader implications of environmental factors on mental health outcomes. The…

Scientific Questions Being Studied

The specific scientific question I intend to study is: "How does air pollution impact mental health across different populations in the United States?" This question is crucial for understanding the broader implications of environmental factors on mental health outcomes. The study aims to uncover potential correlations between varying levels of air pollution exposure and the prevalence of mental health issues, such as anxiety, depression, and cognitive decline. This research is important because it can provide valuable insights into the public health burden of air pollution, guiding interventions and policies to mitigate these effects. Exploring this data will help formalize the research question and identify key variables, ultimately contributing to a deeper understanding of how air quality influences mental well-being. The findings could lead to improved air quality standards and mental health resources, addressing an urgent need for integrated environmental and health policies.

Project Purpose(s)

  • Disease Focused Research (disease of mental health)

Scientific Approaches

For my study on the relationship between air pollution and mental health, I will use a multidisciplinary scientific approach integrating environmental science, epidemiology, and data analytics. I will utilize large-scale datasets, including satellite-derived air pollution measurements (e.g., PM2.5 levels), health records, and mental health surveys from sources like the CDC and EPA. Geospatial analysis tools such as ArcGIS and QGIS will be employed to map and analyze pollution exposure across different regions.

I will conduct statistical analyses using software like R and Python to identify correlations between air pollution levels and mental health outcomes. Methods will include regression analysis to adjust for confounding variables, and time-series analysis to observe trends over time. Machine learning techniques will be used to predict mental health risks based on pollution exposure.

Anticipated Findings

The anticipated findings from my study are expected to reveal a significant correlation between higher levels of air pollution and increased prevalence of mental health issues, such as anxiety, depression, and cognitive impairments. I anticipate identifying specific populations and regions that are more vulnerable to these adverse effects, highlighting disparities in mental health outcomes related to air quality.

These findings will contribute to the body of scientific knowledge by providing empirical evidence of the link between environmental factors and mental health. This research will underscore the importance of considering mental health impacts in air quality regulations and public health strategies. Additionally, it will offer insights for policymakers and healthcare providers to develop targeted interventions aimed at mitigating the mental health risks associated with air pollution.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Yanhong Huang - Graduate Trainee, University of New Mexico and University of New Mexico Health Sciences Center

Duplicate of Duplicate of Discrimination, Depression, Suicide

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Scientific Questions Being Studied

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational

Scientific Approaches

We will use the COVID-19 Participant Experience (COPE) survey and the Patient Health Questionnaire (PHQ-9). We will conduct mixed effects modeling and lagged analyses. We may also conduct mediation analyses.

Anticipated Findings

We anticipate that people who experience higher levels of discrimination will be more likely to have increased symptoms of depression and suicidality.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

  • Sarah Lee - Graduate Trainee, University of Massachusetts Medical School
  • Inbar Plaut - Research Fellow, University of Massachusetts Medical School
  • Alexander Wilkins - Other, University of Massachusetts Medical School

Isotretinoin-HS

Does Isotretinoin prescription for patients with acne increase the risk of incident hidradenitis suppurativa? It is important to know the potential ramifications of isotretinoin, the mainstay treatment for severe acne.

Scientific Questions Being Studied

Does Isotretinoin prescription for patients with acne increase the risk of incident hidradenitis suppurativa? It is important to know the potential ramifications of isotretinoin, the mainstay treatment for severe acne.

Project Purpose(s)

  • Drug Development

Scientific Approaches

A retrospective cohort study of patients with acne recieving isotretinoin and a control group recieving oral antibiotics. patients will be monitored longitudinally for new onset HS

Anticipated Findings

Patients recieving isotretinoin are expected to display higher incidence of HS. Dermatologists should be aware of this when pescribing this drug to patients at risk. otherrisk factors for HS in patients with acne will be identified.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Danielle bar - Early Career Tenure-track Researcher, Tel Aviv University

COVID-19 Mental Health Moderators and Mixtures

We intend to identify factors that moderate the relationship between social distancing policies/behaviors and mental health (e.g., depression, anxiety) in All of Us COPE survey participants. We will also study how different policies/behaviors combine to influence mental health.

Scientific Questions Being Studied

We intend to identify factors that moderate the relationship between social distancing policies/behaviors and mental health (e.g., depression, anxiety) in All of Us COPE survey participants. We will also study how different policies/behaviors combine to influence mental health.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

We will primarily use COPE survey data linked to demographic data from the basics survey, and linked area-level data on policies where relevant. We will apply analyses to detect heterogeneity and mixtures in the relationship between social distancing policies/behaviors and mental health.

Anticipated Findings

We anticipate that findings will reveal demographic and individual-level factors that distinguish people whose mental health is particularly impacted by social distancing policies/behaviors during a pandemic, flagging those who may benefit from greater support, and specific sets of policies that are most influential.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham

Collaborators:

  • Yu Zhou - Project Personnel, Mass General Brigham
  • Devon Watts - Research Fellow, Mass General Brigham
  • Chris Kennedy - Early Career Tenure-track Researcher, Mass General Brigham

AI Risk Prediction

The goal of this research project is to create an AI-based risk prediction model to study nicotine dependence among teenagers and adults. We want to test out the effectiveness of AI-based models compared to traditional linear or logistic regression models…

Scientific Questions Being Studied

The goal of this research project is to create an AI-based risk prediction model to study nicotine dependence among teenagers and adults. We want to test out the effectiveness of AI-based models compared to traditional linear or logistic regression models when predicting for nicotine dependence. This information can be useful for educating the public about nicotine dependence and what factors are the most likely signs of nicotine dependence.

Project Purpose(s)

  • Population Health

Scientific Approaches

We plan to use the datasets that involve all races and age ranges in order to better understand what characteristics are the best predictors for nicotine dependence. Research methods used will be multivariate linear and logistic regression along with AI-based machine learning. We will use both to compare the effectiveness.

Anticipated Findings

Anticipated findings are figuring out whether AI-based machine learning will be more useful compared to traditional methods. If AI-based findings are useful, then we will have a better model in predicting for nicotine dependence. This resource will be useful for educators and anyone interested in knowing the most useful predictors for nicotine dependence.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Kejin Dong - Undergraduate Student, University of Florida

Collaborators:

  • Qing Lu - Late Career Tenured Researcher, University of Florida
  • Kevin Zheng - Undergraduate Student, Duke University

Duplicate of AIM-AHEAD-Shared Continue (on CYLIAO)

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Scientific Questions Being Studied

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Control Set

Scientific Approaches

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Anticipated Findings

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Yang Yang - Undergraduate Student, Georgia Institute of Technology
  • Tian Liu - Graduate Trainee, Texas A&M University
  • Che-Yi Liao - Graduate Trainee, Georgia Institute of Technology

CVD

My research focuses on understanding the molecular mechanism underlying heart disease, particularly in identifying biomarkers for early detection and targets for therapeutic intervention. The primary scientific questions I aim to address include: What specific molecular pathways contribute to the development…

Scientific Questions Being Studied

My research focuses on understanding the molecular mechanism underlying heart disease, particularly in identifying biomarkers for early detection and targets for therapeutic intervention. The primary scientific questions I aim to address include: What specific molecular pathways contribute to the development and progression of heart disease? How can these biomarkers be used to predict disease risk and outcomes?

This research is crucial because heart disease remains the leading cause of death globally, posing public health challenges. Early detection and personalized treatment strategies could drastically reduce morbidity and mortality rates. By elucidating the molecular basis of heart disease, we can develop more effective diagnostic tools and targeted therapies, ultimately improving patient outcomes and reducing healthcare costs. This work has the potential to transform our approach to preventing and treating heart disease, making it highly relevant to both science and public health.

Project Purpose(s)

  • Disease Focused Research (heart disease)

Scientific Approaches

To investigate heart disease, I will use a combination of molecular biology techniques, bioinformatics tools, and comprehensive data analysis methods. Utilizing the All of Us Research Program dataset, which includes diverse health information and biological samples, I aim to identify biomarkers and molecular pathways associated with heart disease. Methods will include proteomics, metabolomics, RNA sequencing, and advanced bioinformatics analyses using machine learning algorithms to detect patterns and correlations. Statistical analysis will ensure the reliability of results. Tools such as deep learning, data integration platforms, and visualization software will facilitate a comprehensive understanding of the molecular mechanisms underlying heart disease. This multidisciplinary approach aims to improve early detection and develop targeted therapies, significantly impacting public health.

Anticipated Findings

Anticipated findings from the study include the identification of novel biomarkers and molecular pathways associated with heart disease, which will enhance our understanding of its development and progression. We expect to discover specific molecular signatures that can predict disease risk and outcomes, enabling earlier detection and personalized treatment strategies. These findings will contribute significantly to the body of scientific knowledge by providing new insights into the molecular underpinnings of heart disease. This research has the potential to transform current diagnostic and therapeutic approaches, leading to more effective interventions and improved patient outcomes. Additionally, it will provide a valuable resource for the scientific community, facilitating further research and innovation in cardiovascular health. Ultimately, our work aims to reduce the global burden of heart disease and improve public health outcomes.

Demographic Categories of Interest

  • Race / Ethnicity
  • Gender Identity
  • Sexual Orientation
  • Disability Status

Data Set Used

Registered Tier

Research Team

Owner:

  • Chenkai Wu - Early Career Tenure-track Researcher, Duke University

JWang Workspace

To analyze the effect of political instability on health outcomes with a particular emphasis on morbidity and mortality.

Scientific Questions Being Studied

To analyze the effect of political instability on health outcomes with a particular emphasis on morbidity and mortality.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

Machine learning models with time series analysis on FitBit data, ICD-10 codes, hospital admissions, and deaths.

Anticipated Findings

We hypothesize that political instability is positively correlated with worse health outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Neil Kelly - Research Fellow, University of Pittsburgh
  • Jesse Wang - Research Fellow, University of Pittsburgh

Collaborators:

  • Wadih El Khoury - Research Fellow, University of Pittsburgh

AIM-AHEAD-Shared Continue

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Scientific Questions Being Studied

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Control Set

Scientific Approaches

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Anticipated Findings

Design and Assessment of Fair Algorithms for Counterfactual Explanations to Generate Digital Role Models for Patients with Type-2 Diabetes and Hypertension

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Yang Yang - Undergraduate Student, Georgia Institute of Technology
  • Tian Liu - Graduate Trainee, Texas A&M University
  • Che-Yi Liao - Graduate Trainee, Georgia Institute of Technology

Original Workshop: Intro to All of Us Electronic Health Records Data

The hands-on workshop covers the following topics: an overview of electronic health records (EHR) data, how EHR data are structured, stored, and standardized on the All of Us Researcher Workbench, how to build cohorts and datasets with EHR data on…

Scientific Questions Being Studied

The hands-on workshop covers the following topics: an overview of electronic health records (EHR) data, how EHR data are structured, stored, and standardized on the All of Us Researcher Workbench, how to build cohorts and datasets with EHR data on the Researcher Workbench, and how to analyze EHR data on the Researcher Workbench. By working through the exercises in this workspace, users will become more familiar with All of Us EHR data and learn how to perform EHR data analysis on the Workbench.

Project Purpose(s)

  • Other Purpose (This workspace is intended to provide an introduction to working with electronic health records data on the All of Us Researcher Workbench. )

Scientific Approaches

We will use the Cohort/Dataset builder and Jupyter notebook to create a cohort and analyze EHR data. Specifically, we will investigate whether there is a temporal trend in A1C values leading up to the time of an electrocardiogram among participants with type 2 diabetes.

Anticipated Findings

We anticipate that workshop attendees will understand how EHR data are stored and standardized on the Researcher Workbench. In addition, they will learn how to build cohorts and analyze longitudinal EHR data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Cathy Shyr - Research Fellow, Vanderbilt University Medical Center

Mental health CVD

Is there a relationship between mental health disorders and cardiovascular outcomes? Does a diagnosis of a mental health disorder contribute to worse cardiovascular outcomes?

Scientific Questions Being Studied

Is there a relationship between mental health disorders and cardiovascular outcomes? Does a diagnosis of a mental health disorder contribute to worse cardiovascular outcomes?

Project Purpose(s)

  • Disease Focused Research (arteriosclerotic cardiovascular disease, heart failure, atrial fibrillation)

Scientific Approaches

To evaluate the relationship between mental health disorders and cardiovascular outcomes. It will label individuals with documented depression, anxiety disorders, bipolar disorder, PTSD, and schizophrenia as positive for “Mental Health Disorder" (MHD) and the development of MI, stroke, and/or heart failure as an adverse cardiovascular outcome. The study will isolate individuals with a negative history of CVD and group them based on a positive or negative diagnosis of MHD. These two groups will then be further analyzed by development of cardiovascular outcome to determine if there is a significant difference. For background information, it will also include a cross-sectional analysis to determine the prevalence of MHD and individuals with a history of cardiovascular outcomes.
The study will also include sub-group analyses to determine whether having MHD in addition to another high-risk condition has an additional effect.

Anticipated Findings

It is expected that there is a direct relationship between MHDs and adverse cardiovascular outcomes. Individuals with MHDs are less likely to receive medical care and may be less likely to live healthy lifestyles that decrease one’s risk for cardiovascular disease. If a relationship is established between MHDs and adverse cardiovascular outcomes, this would further the understanding of the direct physical toll of MHDs. It would also indicate that treating MHDs is a form of primary prevention of cardiovascular disease, which would have a positive benefit for individual patients while also decreasing the significant healthcare costs associated with cardiovascular disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Zhiqi Yao - Research Fellow, Johns Hopkins University

E-cig project

We will evaluate the general epidemiology for e-cigarette use and we will study the relationships with other key variables having to do with life stressors of other lifestyle habits. Besides, we will explore whether e-cig use can lead to cardiovascular…

Scientific Questions Being Studied

We will evaluate the general epidemiology for e-cigarette use and we will study the relationships with other key variables having to do with life stressors of other lifestyle habits. Besides, we will explore whether e-cig use can lead to cardiovascular health effects.
E-cigarette use has gained popularity, with the patterns of use changing rapidly, raising concerns about its potential health consequences. By studying the current epidemiology and investigating the relationship between e-cigarette use and other lifestyle traits, we can shed light on the overall health implications of e-cigarette use. Moreover, exploring the association between e-cigarette use and incident hypertension is critical due to the growing prevalence of both e-cigarette use and hypertension, particularly in young individuals. Understanding this potential link can guide public health policies, healthcare strategies, and health education efforts to mitigate hypertension-related risks among e-cigarette users.

Project Purpose(s)

  • Disease Focused Research (CVD )
  • Population Health
  • Social / Behavioral

Scientific Approaches

We will utilize the extensive All of Us dataset as our primary data source. With its diverse participant pool and comprehensive data on demographics and health-related variables, the dataset offers a robust foundation for our research. We intend to utilize relevant baseline variables, including e-cigarette use, lifestyle variables including psychological stressors, and baseline hypertension status. The analyses for our first aim will be cross-sectional, employing logistic regression and related methods. For the second aim, we will ascertain incident health status – specifically incident hypertension – from the data derived from linked EHR files. These analyses will employ Cox proportional hazards regression and related survival analysis techniques. Our team's multidisciplinary expertise equips us to conduct and oversee rigorous analysis. In addition to our primary research questions, we will broaden our analysis to encompass other health outcomes related to e-cigarette use.

Anticipated Findings

The study aims to explore plausible associations between e-cigarette use, and other lifestyle patterns including other tobacco use and illicit drug use, psychosocial stressors, and incident hypertension, leveraging the All of Us dataset. The anticipated outcomes may unveil correlations and associations between e-cigarette use and heightened psychosocial stress levels, coupled with an increased risk of developing hypertension. These findings could expand our current understanding of the health implications of e-cigarettes, potentially guiding proactive strategies and targeted interventions. The results have the potential to enhance scientific insights by untangling the intricate pathways linking e-cigarette use, psychosocial determinants, and the occurrence of hypertension. As a result, this contribution could inform evidence-driven approaches in public health, potentially stimulating further exploration within this rapidly evolving research domain.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

  • Zhiqi Yao - Research Fellow, Johns Hopkins University

Collaborators:

  • Erfan Tasdighi - Research Fellow, Johns Hopkins University

Characterizing human mutation rates

Our lab is interested in the processes by which human genetic variation is generated and maintained. This project aims to characterize the rate at which genetic variation is generated through the process of mutation, where differences in DNA occur and…

Scientific Questions Being Studied

Our lab is interested in the processes by which human genetic variation is generated and maintained. This project aims to characterize the rate at which genetic variation is generated through the process of mutation, where differences in DNA occur and are inherited.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will identify de novo mutations in the All of Us cohort.

Anticipated Findings

Through the diverse cohort of All of Us, we will study the sources of germline mutation and its phenotypic and fitness consequences.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Lin Poyraz - Graduate Trainee, Columbia University

autoimmune

We want to understand specific mutations in the population of the autoimmune diseases in the context of the UBA6 gene variants

Scientific Questions Being Studied

We want to understand specific mutations in the population of the autoimmune diseases in the context of the UBA6 gene variants

Project Purpose(s)

  • Disease Focused Research (autoimmune disease of skin and connective tissue)
  • Drug Development
  • Methods Development
  • Ancestry

Scientific Approaches

We will find cohort of patients of autoimmune disease and understand their genetic variant around uba6 over-represented in the patient samples.

Anticipated Findings

If autoimmune causal variants are found in uba6, that would increase the knowledge of the autoimmune research especially related to the UBA6 gene.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Yong Eun - Research Associate, New York City Health & Hospitals

How to use dsub in the Researcher Workbench (v7)

The purpose of this workspace is to demonstrate how to use dsub within the Researcher Workbench. This workspace will demonstrate writing dsub jobs.

Scientific Questions Being Studied

The purpose of this workspace is to demonstrate how to use dsub within the Researcher Workbench. This workspace will demonstrate writing dsub jobs.

Project Purpose(s)

  • Educational

Scientific Approaches

The purpose of this workspace is to demonstrate how to use dsub within the Researcher Workbench. This workspace will demonstrate writing dsub jobs.

Anticipated Findings

N/A

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

1 - 25 of 11354
<
>
Request a Review of this Research Project

You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.