Research Projects Directory

Pathogenic allele frequency - exploration

I am exploring the dataset for likely pathogenic and pathogenic alleles for variety of genetic diseases (e.g. Sickle Cell disease). The question I hope to answer using the data is: what is the frequency of pathogenic alleles for a specific…

Scientific Questions Being Studied

I am exploring the dataset for likely pathogenic and pathogenic alleles for variety of genetic diseases (e.g. Sickle Cell disease). The question I hope to answer using the data is: what is the frequency of pathogenic alleles for a specific disease?

Project Purpose(s)

Ancestry

Scientific Approaches

I plan to use the data in the All of Us Research biobank to determine the frequency of pathogenic alleles for a variety of genetic diseases.

Anticipated Findings

I anticipate that the broad and diverse sample set provided by the All of Us Research database will provide the most updated pathogenic allele frequency.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jennifer Cohen - Early Career Tenure-track Researcher, Duke University

Collaborators:

Megan Ramaker - Research Associate, Duke University

PheWAS (V8)

HbA1c is a clinical measure used to assess glycemic control over time. However some genetic variants may interfere with the accuracy of HbA1c as a measure of glycemic control. This could have negative implications for management of patients with diabetes/prediabetes…

Scientific Questions Being Studied

HbA1c is a clinical measure used to assess glycemic control over time. However some genetic variants may interfere with the accuracy of HbA1c as a measure of glycemic control. This could have negative implications for management of patients with diabetes/prediabetes and lead to an increased risk of complications. This is particularly an issue for individuals with ancestry from malaria endemic regions, whose genomes may contain high impact variants in genes such as G6PD and HBB (i.e. sickle cell trait) that have been under selective pressure from malaria in the past and may now be interfering with accurate clinical use of the HbA1c measure. Our goal for this study is to characterize whether variants in G6PD and HBB impact the rate of diabetes related complications, likely due to impacts on HbA1c measurement accuracy.

Project Purpose(s)

Disease Focused Research (Diabetes)
Ancestry

Scientific Approaches

- Datasets: those with type 1 or 2 diabetes and WGS
- Sickle Cell status rs334(A;T) - yes or no
- G6PD variants – where males are multiplied by two

Hypothesis 1: Individuals with diabetes and with known G6PD coding variants, particularly hemizygous males or homozygous females, will have a higher rate of diabetic retinopathy.
Cox proportional hazards models:
1. outcome~age+ sex + G6PD variant count+ sickle cell trait status + 10 principal components of genetic ancestry
2. + BMI

Hypothesis 2: In individuals with diabetes, HbA1c will be more predictive of retinopathy when adjusted for G6PD coding variant status. All models should be stratified by diabetes status (any diabetes, including either type 1 or type 2) at beginning of follow-up.
Cox proportional hazards models:
1. outcome~HbA1c+age+ sex + G6PD variant count+ sickle cell trait status + 10 principal components of genetic ancestry
2. +BMI

Anticipated Findings

We anticipate that coding variants in G6PD and HBB will lead to increased diabetic retinopathy. Understanding how variants from diverse populations impact our clinical measures and outcomes is imperative for reducing racial health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Micah Hysong - Graduate Trainee, University of North Carolina, Chapel Hill

Collaborators:

Harshita Dasari - Undergraduate Student, University of North Carolina, Chapel Hill

SCT & PIEZO1 Variant in Relationship to CKD (V8)

What is the relationship between SCT and the PIEZO1 variant and CKD? Sickle cell trait (SCT) has been shown to be associated with chronic kidney disease (CKD). However, the statistical strength of association varies between study populations/studies. One potential reason…

Scientific Questions Being Studied

What is the relationship between SCT and the PIEZO1 variant and CKD?
Sickle cell trait (SCT) has been shown to be associated with chronic kidney disease (CKD). However, the statistical strength of association varies between study populations/studies. One potential reason for these heterogenous findings may be the differential distribution of the PIEZO1, a functional variant that modulates cellular ion channels and may increase the risk of dehydration. Dehydration is of particular concern in SCT patients because their red blood cells (RBC) have slight structural deficiencies that are exacerbated under physiological stressful conditions (including dehydration), which may lead to polymerization (the proximal cause of sickling). Sickling is known to significantly increase the risk of CKD, as widely observed in SCD patients. We will create a cohort of individuals with and without SCT, follow them until censoring and analyze PIEZO1 variant as an interaction term with SCT.

Project Purpose(s)

Disease Focused Research (Chronic Kidney Disease; Sickle Cell Trait)

Scientific Approaches

We will develop a cohort of individuals with and without SCT (indexed at date of enrollment) and follow them prospectively until censoring (outcome development or administrative cut-off). To assess if PIEZO1 variant increases the adverse effect of SCT, we will treat the PIEZO1 variant as an interaction term with SCT. To create this cohort, we plan to use the genomic data (Controlled Tier), EHR data (Registered Tier), and Lab data where available (Controlled Tier).

Anticipated Findings

This study aims to contribute to the body of scientific knowledge by improving understanding of a suspected predictor (SCT) of CKD. Finding may help improve risk assessment, screening practices, and prevention strategies to preempt severity and progression of CKD. Earlier diagnosis can decrease illness burden and improve overall trajectory in CKD.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Kimi Van Wickle - Graduate Trainee, University of North Carolina, Chapel Hill

Collaborators:

Emily Salerno - Graduate Trainee, University of North Carolina, Chapel Hill

Sickle Cell Trait Associated Clinical Outcomes (V8)

Sickle cell trait (SCT) is largely a benign carrier state; however, a growing body of research has reported evidence on associated clinical complications. The primary purpose of this project is to identify differences in frequency of reported SCT associated clinical…

Scientific Questions Being Studied

Sickle cell trait (SCT) is largely a benign carrier state; however, a growing body of research has reported evidence on associated clinical complications. The primary purpose of this project is to identify differences in frequency of reported SCT associated clinical outcomes in a cohort of SCT and non-SCT carriers. In addition, social and environmental factors will be examined to identify potential risk modifiers of the carrier state. Specific questions for this project include:

1. Does the prevalence of clinical complication differ between SCT and non-SCT carriers?
2. What factors may increase the risk of developing clinical outcomes among SCT carriers?

As our understanding of SCT continues to develop, accurately assessing possible related clinical complications and the factors that put carriers at higher risk of developing them will assist healthcare providers with counseling and treatment for individuals who are carriers for sickle cell anemia (sickle cell trait).

Project Purpose(s)

Disease Focused Research (Sickle Cell Trait )
Ancestry

Scientific Approaches

In this study, electronic health records (EHRs) and survey data of identified SCT carriers will be analyzed to assess frequency of reported SCT associated clinical outcomes to help strengthen or refute current evidence on SCT association. High frequency of clinical outcomes with no reported association to SCT will also be accounted for.
EHRs and survey data for a comparison group of All of Us participants that do not have SCT will also be assessed, to identify any significant differences in SCT associated clinical complication manifestation. Additionally, EHRs and survey data will be analyzed to explore factors identified within SCT literature that may put carriers at higher risk of developing complications. We will use R to complete all analysis.

Anticipated Findings

A poor understanding of SCT has contributed to its stigmatization, affecting screening efforts, policy decisions, and clinical treatment of carriers. By utilizing a large sample of confirmed SCT carriers, our study sets out to provide a better understanding of the carrier state in an effort to combat stigma and misinformation that has plagued this population in the past. In addition, our findings will report of the rarity of complications among this population. We anticipate that our findings will strengthen current evidence on the association or not of certain clinical outcomes with SCT. Overall, we hope that our findings will provide a better understanding of this carrier state, to improve health care providers and individuals living with sickle cell trait knowledge of the carrier state.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Hasmin Ramirez - Project Personnel, National Human Genome Research Institute (NIH - NHGRI)

_v8_ Sickle cell disease and chronic comorbidities

This project explores patterns and sequencing of multimorbidity among people living with SCD in the US. Middle-aged and older adults living with sickle cell disease (SCD) are an emerging demographic in the United States. In the absence of a widely…

Scientific Questions Being Studied

This project explores patterns and sequencing of multimorbidity among people living with SCD in the US. Middle-aged and older adults living with sickle cell disease (SCD) are an emerging demographic in the United States. In the absence of a widely available and affordable SCD cure, hypothesis-generating research is needed to identify potential avenues for promoting capability-focused aging when living with this lifespan-limiting condition. Existing research documents that people with SCD often manage multiple chronic conditions concurrently. However, less is known about the average timing onset of multimorbidity, the ordering of disease trajectories, and their associations with mortality – especially in contrast to individuals with sickle cell trait (SCT).

Project Purpose(s)

Disease Focused Research (Sickle cell disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

This study will use sequence analysis in addition to other statistical methods to identify trajectories of chronic disease onset among people with SCD, SCT, and unaffected controls participating in the All of US registry. The initial phase of this research project is exploratory; as the project progresses the scientific questions will be refined and addressed with appropriate quantitative approaches.

Anticipated Findings

We anticipate that findings from this research will allow us to describe the process of transitioning to the state of having multiple chronic conditions among people with SCD. Identifying potential differences in chronic disease trajectories using in All of US data we hope to gain insight into lifestyle, medication, and/or health management factors that could be tested in future studies to pre-empt disease and promote longevity.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Elizabeth Linton - Research Fellow, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

Gabriel Goodney - Project Personnel, National Heart, Lung, and Blood Institute (NIH - NHLBI)
Jun Qian - Other, All of Us Program Operational Use

SCT Demographics

I intend to study those with Sickle Cell Trait and identify commonalities to use for early detection of the disease, to increase education and access to early treatment.

Scientific Questions Being Studied

I intend to study those with Sickle Cell Trait and identify commonalities to use for early detection of the disease, to increase education and access to early treatment.

Project Purpose(s)

Disease Focused Research (Sickle Cell Trait)

Scientific Approaches

I plan to use Python, Jupyter, Pandas, NumPy and SciPy for this study. This study will look at datasets of information on Sickle Cell Trait.

Anticipated Findings

I anticipate to find additional information on individuals that have Sickle Cell Trait or at risk of having Sickle Cell Trait and not knowing.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

CJ Bingham - Undergraduate Student, Florida Atlantic University

sickle_cell_traits

The primary scientific questions this study aims to answer are: How many people with Sickle Cell Trait (SCT) are unaware of their condition? What are the primary health risks associated with SCT in high-exertion environments like military service and athletics?…

Scientific Questions Being Studied

The primary scientific questions this study aims to answer are:
How many people with Sickle Cell Trait (SCT) are unaware of their condition?
What are the primary health risks associated with SCT in high-exertion environments like military service and athletics?
Are there specific demographic patterns or risk factors associated with SCT that can be identified using the All of Us database?
How can technology improve awareness, screening, and risk assessment for individuals with SCT?

Project Purpose(s)

Educational

Scientific Approaches

Datasets & Sources
All of Us Research Program Database: Provides demographic, genetic, and health records to analyze SCT prevalence and awareness levels.
Military and Athletic Health Records: If accessible, these datasets can provide insights into exertional-related complications among individuals with SCT.
CDC & NIH Data: Used for baseline statistics on SCT prevalence and known risk factors.

Research Methods
Descriptive Analysis: Determine the proportion of individuals with SCT who are unaware of their condition.
Risk Analysis: Identify health complications associated with SCT under high-exertion conditions.

Anticipated Findings

Expected Outcomes:
A quantifiable estimate of how many people with SCT are unaware of their condition.
Identification of the most significant risk factors and demographic trends associated with SCT complications in high-exertion environments.
A predictive model that can assess an individual’s risk based on health data.
Recommendations on how digital tools (e.g., mobile screening apps, wearables) can improve SCT awareness and risk assessment.

Scientific & Public Health Contributions:
Enhancing Early Detection: Findings could support targeted screening efforts to ensure individuals know their SCT status earlier.
Reducing Health Risks: By identifying high-risk individuals, intervention strategies (such as modified training regimens for athletes or military personnel) can be developed.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Normil Luccin - Graduate Trainee, Florida Atlantic University

Hackathon

Our study aims to investigate predictive factors for sickle cell crisis using patient health data. The primary scientific question is: “Can we accurately predict the onset of a sickle cell crisis based on key clinical and environmental factors?” This question…

Scientific Questions Being Studied

Our study aims to investigate predictive factors for sickle cell crisis using patient health data. The primary scientific question is:
“Can we accurately predict the onset of a sickle cell crisis based on key clinical and environmental factors?”

This question is crucial for public health because sickle cell crises are severe pain episodes that often lead to hospitalizations. Early prediction can help patients and healthcare providers take preventive measures, improving quality of life and reducing healthcare burdens.

At this stage, we are exploring the data to identify key risk factors such as hemoglobin levels, oxygen saturation, heart rate, and environmental conditions (e.g., temperature, humidity). Through machine learning techniques, we hope to develop a risk assessment model that can provide early warnings to patients.

By answering this question, we aim to contribute to precision medicine and preventive healthcare strategies for sickle cell disease.

Project Purpose(s)

Educational

Scientific Approaches

To answer our research question—“Can we accurately predict the onset of a sickle cell crisis based on key clinical and environmental factors?”—we will use a combination of machine learning (ML), exploratory data analysis (EDA), and statistical modeling.

Anticipated Findings

We anticipate that our study will identify key clinical and environmental factors that contribute to the onset of sickle cell crises. Specifically, we expect to find that:

Low oxygen saturation, high heart rate, and low hemoglobin levels are strong predictors of an impending crisis.
Environmental factors such as extreme temperatures and high humidity may increase the risk of a crisis.
Machine learning models can provide an accurate early warning system for patients and healthcare providers.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

James King - Undergraduate Student, Florida Atlantic University

SIckle Cell Group

We intend to investigate the following specific scientific questions: How do genetic factors, such as the presence of sickle cell mutations (e.g., HbS, HbC), influence the severity and frequency of sickle cell crises in patients? This question is vital as…

Scientific Questions Being Studied

We intend to investigate the following specific scientific questions:

How do genetic factors, such as the presence of sickle cell mutations (e.g., HbS, HbC), influence the severity and frequency of sickle cell crises in patients?

This question is vital as it could help identify specific genetic markers associated with more severe manifestations of sickle cell disease (SCD), potentially leading to more targeted therapies or personalized treatments.
What is the impact of demographic factors (such as age, gender, and ethnicity) on the healthcare outcomes and complications in individuals with sickle cell disease?

Project Purpose(s)

Disease Focused Research (sickle cell anemia)
Social / Behavioral
Educational
Methods Development
Ancestry

Scientific Approaches

We will use data analysis, predictive modeling, and survival analysis to investigate sickle cell disease. Our methods include:

Exploratory Data Analysis (EDA) to understand the dataset and identify patterns.
Statistical analysis (correlations, t-tests) to examine relationships between genetic and clinical factors.
Machine learning models (e.g., random forests, SVM) to predict complications (e.g., stroke, organ failure).
Survival analysis to predict long-term outcomes.
Datasets:

We will use the ResearchAllofUs dataset, which includes demographic, clinical, genetic, and treatment data on sickle cell patients.

Tools:

We will utilize Python (Pandas, Scikit-learn, Lifelines) for data cleaning, statistical analysis, and machine learning, and R for advanced statistical tests and visualizations.

These methods will help uncover factors influencing sickle cell severity and complications, supporting better-targeted treatments and interventions.

Anticipated Findings

We anticipate that our study will uncover key genetic, demographic, and clinical factors that influence the severity and progression of sickle cell disease (SCD). We expect to identify specific genetic mutations (e.g., HbS, HbC) and demographic characteristics (age, ethnicity) that correlate with more severe disease outcomes, such as frequent pain crises, strokes, and organ damage. Additionally, our predictive models could provide insights into which factors are most influential in forecasting complications.

Our findings will contribute to the body of scientific knowledge by improving the understanding of the genetic and environmental determinants of SCD. This could lead to more personalized treatment strategies, better management of disease progression, and improved prediction of complications. Furthermore, our work may inform future research on targeted interventions and healthcare policies aimed at reducing SCD-related mortality and improving patient outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Freddy Moreno - Graduate Trainee, Florida Atlantic University

CCDSG_Alloimmunization_genetic_risk_2025

Our scientific question is to find out if a certain gene carriers (based on previous study) has an increased risk to develop alloimmunization among patients with sickle cell diseases. Because we need to select a subset of patients with sickle…

Scientific Questions Being Studied

Our scientific question is to find out if a certain gene carriers (based on previous study) has an increased risk to develop alloimmunization among patients with sickle cell diseases. Because we need to select a subset of patients with sickle cell disease who received blood transfusion. We need to obtain their medical history data to see if they developed the alloimmunization within a certain period. We also need to exclude certain patients who has other comorbidities that will have a confounding effects on the alloimmunization outcome. We will need to see if the dataset we query from the CDR reflect a nature history of the disease progression, and decide if a case control design or a survival type of analysis is more appropriate.

Project Purpose(s)

Educational

Scientific Approaches

We plan to query the condition and procedure domain to build our cohort. We also plan to query the drug, labs and measurements tables to see if we can build a disease progression history that can help us to pinpoint the correct cohort.

Anticipated Findings

We will be able to see if our query will produce adequate dataset with variable that represents our study cohort. This will help us to see if we can use the EHR data to track the alloimmunization disease progression. To couple with genomic data, we will be able to find out if genetic play an important role in the alloimmunization.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Qing Li - Research Associate, National Human Genome Research Institute (NIH - NHGRI)

CCDSG_Alloimmunization_genetic_risk_2023

Our scientific question is to find out if a certain gene carriers (based on previous study) has an increased risk to develop alloimmunization among patients with sickle cell diseases. Because we need to select a subset of patients with sickle…

Scientific Questions Being Studied

Our scientific question is to find out if a certain gene carriers (based on previous study) has an increased risk to develop alloimmunization among patients with sickle cell diseases. Because we need to select a subset of patients with sickle cell disease who received blood transfusion. We need to obtain their medical history data to see if they developed the alloimmunization within a certain period. We also need to exclude certain patients who has other comorbidities that will have a confounding effects on the alloimmunization outcome. We will need to see if the dataset we query from the CDR reflect a nature history of the disease progression, and decide if a case control design or a survival type of analysis is more appropriate.

Project Purpose(s)

Educational

Scientific Approaches

We plan to query the condition and procedure domain to build our cohort. We also plan to query the drug, labs and measurements tables to see if we can build a disease progression history that can help us to pinpoint the correct cohort.

Anticipated Findings

We will be able to see if our query will produce adequate dataset with variable that represents our study cohort. This will help us to see if we can use the EHR data to track the alloimmunization disease progression. To couple with genomic data, we will be able to find out if genetic play an important role in the alloimmunization.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Qing Li - Research Associate, National Human Genome Research Institute (NIH - NHGRI)

Duplicate of Hemoglobinopathy Study

The primary aim is to quantify the prevalence of HBP and HBP trait in the multiethnic communities by identifying the number of carriers and affected births for sickle cell disease and beta-thalassemia already registered and estimate the expected births from…

Scientific Questions Being Studied

The primary aim is to quantify the prevalence of HBP and HBP trait in the multiethnic communities by identifying the number of carriers and affected births for sickle cell disease and beta-thalassemia already registered and estimate the expected births from the number of births in the ethnic minority populations and compare these figures to the numbers actually registered.

Project Purpose(s)

Disease Focused Research (Hemoglobinopaties)
Population Health
Control Set

Scientific Approaches

To calculate prevalence, we will use the data to identify thalassemia intermedia/major and sickle cell disease. We will use ICPC codes.
Descriptive statistics such as race, age and gender will also be collected.

Anticipated Findings

Endpoints
Primary:
Quantity the prevalence of HBP and HBP trait by identifying the number of carriers and affected births for sickle cell disease and beta-thalassemia already registered and estimate the expected births from the number of births in the ethnic minority populations and compare these figures to the numbers actually registered.

Secondary: Review the distribution of carriers and patients with HBPs regionally will generate data to apply for external grants and will enable more extensive prospective studies to screen for HbP in high risk populations and decrease inequity/disparities and access to health care resources among US patients.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Kurt Vergunst - Graduate Trainee, Mayo Clinic

Sickle Cell Trait (CDR v8)

Filtering and extracting variants from All of Us data to look at sickle cell traits to identify phenotypes and genotypes and to run PheWAS/GWAS analyses

Scientific Questions Being Studied

Filtering and extracting variants from All of Us data to look at sickle cell traits to identify phenotypes and genotypes and to run PheWAS/GWAS analyses

Project Purpose(s)

Disease Focused Research (Sickle Cell Trait)

Scientific Approaches

Developing workflows and pipelines using HAIL (in Python) to extract variants and conduct PheWAS and GWAS analyses using AoU data for sickle cell traits

Anticipated Findings

The findings from this study can include disease associations linked to sickle cell traits. These findings (and more) would help researchers with experimental design and analysis and to better understand sickle cell traits.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Anas Awan - Graduate Trainee, University of Nebraska Medical Center

SCD & PIEZO1 Variant in Relationship to CKD

What is the relationship between SCT and the PIEZO1 variant and CKD? Sickle cell trait (SCT) has been shown to be associated with chronic kidney disease (CKD). However, the statistical strength of association varies between study populations/studies. One potential reason…

Scientific Questions Being Studied

What is the relationship between SCT and the PIEZO1 variant and CKD?
Sickle cell trait (SCT) has been shown to be associated with chronic kidney disease (CKD). However, the statistical strength of association varies between study populations/studies. One potential reason for these heterogenous findings may be the differential distribution of the PIEZO1, a functional variant that modulates cellular ion channels and may increase the risk of dehydration. Dehydration is of particular concern in SCT patients because their red blood cells (RBC) have slight structural deficiencies that are exacerbated under physiological stressful conditions (including dehydration), which may lead to polymerization (the proximal cause of sickling). Sickling is known to significantly increase the risk of CKD, as widely observed in SCD patients. We will create a cohort of individuals with and without SCT, follow them until censoring and analyze PIEZO1 variant as an interaction term with SCT.

Project Purpose(s)

Disease Focused Research (Chronic Kidney Disease; Sickle Cell Trait)

Scientific Approaches

We will develop a cohort of individuals with and without SCT (indexed at date of enrollment) and follow them prospectively until censoring (outcome development or administrative cut-off). To assess if PIEZO1 variant increases the adverse effect of SCT, we will treat the PIEZO1 variant as an interaction term with SCT. To create this cohort, we plan to use the genomic data (Controlled Tier), EHR data (Registered Tier), and Lab data where available (Controlled Tier).

Anticipated Findings

This study aims to contribute to the body of scientific knowledge by improving understanding of a suspected predictor (SCT) of CKD. Finding may help improve risk assessment, screening practices, and prevention strategies to preempt severity and progression of CKD. Earlier diagnosis can decrease illness burden and improve overall trajectory in CKD.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Kimi Van Wickle - Graduate Trainee, University of North Carolina, Chapel Hill

Sickle Cell Trait (CDR v7)

Filtering and extracting variants from All of Us data to look at sickle cell traits to identify phenotypes and genotypes and to run PheWAS/GWAS analyses

Scientific Questions Being Studied

Filtering and extracting variants from All of Us data to look at sickle cell traits to identify phenotypes and genotypes and to run PheWAS/GWAS analyses

Project Purpose(s)

Disease Focused Research (Sickle Cell Traits)

Scientific Approaches

Developing workflows and pipelines using HAIL (in Python) to extract variants and conduct PheWAS and GWAS analyses using AoU data for sickle cell traits

Anticipated Findings

The findings from this study can include disease associations linked to sickle cell traits. These findings (and more) would help researchers with experimental design and analysis and to better understand sickle cell traits.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Huan Mo - Research Fellow, National Human Genome Research Institute (NIH - NHGRI)
Anas Awan - Graduate Trainee, University of Nebraska Medical Center

Collaborators:

Hasmin Ramirez - Project Personnel, National Human Genome Research Institute (NIH - NHGRI)
Slavina Goleva - Research Fellow, National Human Genome Research Institute (NIH - NHGRI)
Bennett Waxse - Research Fellow, National Institute of Allergy and Infectious Diseases (NIH - NIAID)
Ariel Williams - Other, University of Virginia
Anirudh Kesanapally - Research Assistant, National Human Genome Research Institute (NIH - NHGRI)

Investigating the Relationship Between Sickle Cell and Glaucoma

We will investigate whether the presence of sickle cell disease or trait may be related to the development of glaucoma, age of onset, the severity of disease, or type of glaucoma by using large population data. It is estimated that…

Scientific Questions Being Studied

We will investigate whether the presence of sickle cell disease or trait may be related to the development of glaucoma, age of onset, the severity of disease, or type of glaucoma by using large population data. It is estimated that sickle cell disease affects 100,000 Americans, and sickle cell trait affects 1 to 3 million of all Americans, and around 10% of all African-Americans. African-Americans are also known to be the race most at risk of developing glaucoma. Very few studies have investigated whether a similar relationship exists between sickle cell trait or disease and glaucoma outside of the traumatic setting. This study aims to address this gap by determining the frequency of glaucoma in African-American patients with and without sickle cell disease or trait in a large national clinical database.

Project Purpose(s)

Disease Focused Research (glaucoma)

Scientific Approaches

We will use both an ophthalmic focused database, the IRIS registry, and a systemically oriented database, the All of Us research hub, to identify patients who have sickle cell disease or trait. We will do this by searching for patients with and without an ICD-9 or -10 code containing a sickle cell code (i.e. D57.X, 282.5X, 282.6X), and in patients with sickle cell, stratifying by disease or trait. We will also identify patients with a glaucoma diagnosis code (i.e. H40.X) and determine if there is a higher frequency of these codes in the sickle cell population compared to race-matched controls without sickle cell. We will search codes for severity (i.e. H40.1113 - primary open angle glaucoma, severe stage) to compare severity of disease with presence or absence of sickle cell disease or trait.

Anticipated Findings

We anticipate to identify a relationship between sickle cell disease or trait and glaucoma. We may potentially find that patients with sickle cell develop more severe disease at younger ages. These findings could indicate a need for earlier screening and more aggressive management of glaucoma in patients with sickle cell disease or trait.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Dilru Amarasekera - Teacher/Instructor/Professor, Wills Eye

Collaborators:

Binod Acharya - Project Personnel, Wills Eye

Sickle Cell Disease and Chronic Conditions

The primary scientific question driving this exploration is: How do chronic conditions impact the clinical outcomes, healthcare utilization, and quality of life among patients with sickle cell disease? To address this overarching question, several sub-questions can be formulated: Epidemiological Patterns,…

Scientific Questions Being Studied

The primary scientific question driving this exploration is: How do chronic conditions impact the clinical outcomes, healthcare utilization, and quality of life among patients with sickle cell disease?

To address this overarching question, several sub-questions can be formulated: Epidemiological Patterns, Clinical Outcomes, Healthcare Utilization, Quality of Life, Intervention Strategies. Exploring the data on sickle cell disease and chronic conditions is a critical step towards improving the care and outcomes of SCD patients. By addressing the complex interactions between SCD and chronic conditions, this research aims to provide a comprehensive understanding that informs clinical practice, policy, and personalized medicine. The ultimate goal is to enhance the quality of life and clinical outcomes for SCD patients, ensuring equitable and effective care for this vulnerable population.

Project Purpose(s)

Disease Focused Research (Sickle Cell Disease)
Population Health
Social / Behavioral
Drug Development
Methods Development
Control Set
Ancestry
Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

For my study on sickle cell disease (SCD) and chronic conditions, I will leverage the available datasets. My scientific approaches include:

Datasets: Utilizing electronic health records (EHRs), genomic data, and patient-reported outcomes (PROs) available in the All of Us database to analyze SCD patients with chronic conditions.

Research Methods:

Descriptive statistics to determine prevalence and incidence rates.
Multivariate regression to evaluate the impact of chronic conditions on SCD outcomes.
Genome-wide association studies (GWAS) to identify genetic markers.
Cost-analysis models for healthcare utilization.
Tools:

Bioinformatics tools for genomic data analysis.
Statistical software (R) for data analysis.
These methods will provide insight to the interactions between SCD and chronic conditions, improving patient care and outcomes.

Anticipated Findings

The anticipated findings include identifying the most prevalent chronic conditions among SCD patients, understanding how these conditions exacerbate SCD complications, and uncovering genetic markers associated with increased susceptibility. We expect to find specific chronic conditions that significantly worsen clinical outcomes and patterns in healthcare utilization linked to these conditions.

These findings will contribute to the body of scientific knowledge by:

Providing a comprehensive epidemiological profile of chronic conditions in SCD patients.
Enhancing the understanding of genetic factors influencing SCD and chronic conditions.
Informing the development of targeted, personalized treatment plans.
Guiding healthcare policies to improve resource allocation and patient care.
Ultimately, this study aims to improve the quality of life and clinical outcomes for SCD patients by addressing both SCD and co-occurring chronic conditions comprehensively.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Nelson Lemieux - Senior Researcher, Seven Star Academy
Keesha Roach - Early Career Tenure-track Researcher, University of Tennessee Health Science Center, Memphis
Kosaku Aoyagi - Early Career Tenure-track Researcher, University of Texas at El Paso

Collaborators:

Xueyuan Cao - Mid-career Tenured Researcher, University of Tennessee Health Science Center, Memphis

Representative Phenotype Discovery Methods

We are developing AI methods to support discovery of phenotype to genotype associations in the context of rare disease. The methods are intended to support a gene-to-patient [Seaby 2020] paradigm in which the starting point is identification of an in-silico…

Scientific Questions Being Studied

We are developing AI methods to support discovery of phenotype to genotype associations in the context of rare disease. The methods are intended to support a gene-to-patient [Seaby 2020] paradigm in which the starting point is identification of an in-silico predicted pathogenic variant(s). From there, one identifies a cohort of individuals with the predicted pathogenic variants to determine if they share a common phenotype that may be associated. In this project, we seek to validate the performance of methods we're developing to automate the analysis of individuals in the cohort to identify a representative phenotype. The planned validation process will include the following steps:

1. Select a rare disease with known phenotype and genotype
2. Identify a cohort with the disease. Apply the automated method to the cohort and compare the solution to the known phenotype.

We intend initially to consider Cystic Fibrosis and Sickle Cell Disease. Additional cohorts may be considered.

Project Purpose(s)

Methods Development

Scientific Approaches

Our method uses NLP methods to transform text descriptions of ontology concepts to numerical representations called embeddings. We represent individuals by collections of ontology terms as embeddings. We then use novel methods to identify a subset of ontology terms that best describes the overall cohort. We will apply this method to cohorts with rare disease diagnosis to determine if the method is able to recover the known disease phenotype. Specifically, we will identify individuals with a known rare disease by presence of selected SNOMED-CT diagnoses codes in the Condition Occurrence table or by presence of known disease causing genetic variants. We will then extract signs and symptoms for this cohort and apply our methods. We will conduct statistical analyses to assess method performance. The methods are implemented in Python using a variety of AI techniques.

Anticipated Findings

We anticipate that the results of this study will demonstrate validate the use of our methods in real-world data settings and identify potential limitations for method refinement. Ultimately, we expect that the methods will enable more rapid discovery of genotype-phenotype associations, thereby increasing rare disease diagnostic rates.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Aaron Masino - Early Career Tenure-track Researcher, Clemson University

Collaborators:

Amna Islam - Graduate Trainee, Clemson University
Ranga Baminiwatte - Graduate Trainee, Clemson University

Sickle Cell Disease NYS Outcomes

My research focuses on understanding the disparities in healthcare outcomes for individuals with Sickle Cell Disease (SCD) in New York State. Specifically, I aim to investigate the factors influencing variations in severity of illness, risk of mortality, and healthcare access…

Scientific Questions Being Studied

My research focuses on understanding the disparities in healthcare outcomes for individuals with Sickle Cell Disease (SCD) in New York State. Specifically, I aim to investigate the factors influencing variations in severity of illness, risk of mortality, and healthcare access across different regions and demographic groups. The research is critical for addressing healthcare inequities and improving outcomes for populations disproportionately affected by SCD, particularly among historically underserved communities. By examining regional and facility-level data, this study seeks to inform public health policies and improve SCD care delivery.

Project Purpose(s)

Disease Focused Research (Sickle Cell)
Population Health

Scientific Approaches

This study will utilize a combination of quantitative analyses, using statistical methods such as generalized linear models and regression analysis, to examine associations between healthcare outcomes (severity of illness, risk of mortality, etc.) and various factors like geographical location, demographics, and hospital characteristics. The data will be sourced from the SPARCS dataset, which includes claim transaction IDs, personal identifiers, and ZIP code information. Tools such as R, SAS, and Python will be used to analyze the data, while AWS will provide the secure cloud environment necessary for data storage and computation.

Anticipated Findings

The study anticipates finding significant regional disparities in the management and outcomes of Sickle Cell Disease patients in New York. It is expected that these findings will highlight differences in access to care, severity of illness, and mortality risk based on race, socioeconomic status, and geographic location. These results could contribute to developing targeted interventions, improving healthcare policies, and promoting equity in healthcare delivery, ultimately enhancing the quality of care for underrepresented populations suffering from SCD.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Emeka Iloegbu - Graduate Trainee, New York University

RBC traits and sickle cell disease

To understand how red blood cell traits may vary in sickle cell disease and its clinical complications

Scientific Questions Being Studied

To understand how red blood cell traits may vary in sickle cell disease and its clinical complications

Project Purpose(s)

Disease Focused Research (sickle cell anemia)
Ancestry

Scientific Approaches

I plan to use the summary statistics of red blood cell traits and develop polygenic scores for another dataset

Anticipated Findings

These findings would allow us to understand if hematologic traits can be used to stratify SCD patients with chronic pain

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Varsha Bhat - Graduate Trainee, Georgia Institute of Technology

Postdoc

I am exploring the data to formalize a specific research question. I am interested in possible genetic variants other than HBB that are associated with sickle cell disease.

Scientific Questions Being Studied

I am exploring the data to formalize a specific research question. I am interested in possible genetic variants other than HBB that are associated with sickle cell disease.

Project Purpose(s)

Disease Focused Research (sickle cell anemia)
Ancestry

Scientific Approaches

I plan to use genomic data and social determinant of health. I am not sure if electronic health records data will be helpful, but I will investigate its usefulness.

Anticipated Findings

The study will likely show genes other than HBB that are associated with sickle cell disease. It may also uncover social determinants of health that are associated with this disease.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Brittany Taylor - Research Fellow, University of Pittsburgh

Sickle Cell Disease

I wish to study variants that exist across populations to do with sickle cell disease. I hope to be able to contribute to sickle cell disease treatment and the inclusivity of the treatment.

Scientific Questions Being Studied

I wish to study variants that exist across populations to do with sickle cell disease. I hope to be able to contribute to sickle cell disease treatment and the inclusivity of the treatment.

Project Purpose(s)

Disease Focused Research (Sickle Cell Disease)
Ancestry

Scientific Approaches

I will look at primarily populations of African and European descent, those with and without sickle cell disease.

Anticipated Findings

I anticipate to find variants that segregate across different populations in an observable pattern. Perhaps this pattern will present a novel modifier of sickle cell disease genes that can be targeted for therapeutics.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Fujr Osman Ibrahim Osman - Graduate Trainee, Johns Hopkins University

Sickle_Cell_Disease_2

I wish to study variants that exist across populations to do with sickle cell disease. I hope to be able to contribute to sickle cell disease management and treatment while remaining inclusive.

Scientific Questions Being Studied

I wish to study variants that exist across populations to do with sickle cell disease. I hope to be able to contribute to sickle cell disease management and treatment while remaining inclusive.

Project Purpose(s)

Disease Focused Research (Sickle Cell Disease)
Ancestry

Scientific Approaches

I will analyze the association of genetic variants across individuals with varying phenotypes with sickle cell disease.

Anticipated Findings

I anticipate to find variants that segregate across different populations in an observable pattern. Perhaps this pattern will present a novel modifier of sickle cell disease genes that can be targeted for therapeutics.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Fujr Osman Ibrahim Osman - Graduate Trainee, Johns Hopkins University

Sickle Cell Anemia

What are the current treatments available for sickle cell disorder. How do factor such as age, gender and availability effect treatment course. From current treatment methods, how can these be improved?

Scientific Questions Being Studied

What are the current treatments available for sickle cell disorder. How do factor such as age, gender and availability effect treatment course. From current treatment methods, how can these be improved?

Project Purpose(s)

Disease Focused Research (sickle cell anemia)
Educational

Scientific Approaches

Observe current datasets available. We will use and analyze current research studies and study previous treatment methods.

Anticipated Findings

We anticipate on finding treatment "holes". Our goal is to understand the most optimal treatment approach available for sickle cell disorder.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Matt Teel - Undergraduate Student, Arizona State University
Imerson Macon-Wagner - Undergraduate Student, Arizona State University
Daniel Armstrong - Undergraduate Student, Arizona State University
Angelina Mollins - Undergraduate Student, Arizona State University

Sickle cell disease

Scientific question: How do specific genetic variants in the beta-globulin gene influence the clinical severity of sickle cell disease? This question is important for personalized medicine approaches. It would aid identifying specific gene variants linked to disease severity to be…

Scientific Questions Being Studied

Scientific question: How do specific genetic variants in the beta-globulin gene influence the clinical severity of sickle cell disease? This question is important for personalized medicine approaches. It would aid identifying specific gene variants linked to disease severity to be able to help in developing targeted therapies, It would give knowledge of how genetic differences influence disease outcomes, Insights gained form the research could also inform studies on other genetic disorders. This question not only advances knowledge but potentially improve patient care and outcomes to people with the disease.

Project Purpose(s)

Educational

Scientific Approaches

Some scientific approaches to this research would be Genomic databases, disease specific repositories such as a sickle cell disease genetic network, Gene expression database, statistical analysis database, Gene editing tools, and clinical data management systems. These tools and methods can conduct comprehensive research.

Anticipated Findings

Anticipated findings : Identification of specific gene variants, Gene environment interactions, functional consequences of variants, and understanding the molecular mechanisms. My findings can contribute to personalized treatment approaches.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Matt Teel - Undergraduate Student, Arizona State University

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