Research Projects Directory

Duplicate of Duplicate of ALDH2 HLP

Alcohol consumption is a risk factor for many chronic diseases, including some cancers, type 2 diabetes, and Alzheimer’s disease. Individuals with a specific variant of the aldehyde dehydrogenase 2 gene (ALDH2*2) are at higher risk of many of these diseases.…

Scientific Questions Being Studied

Alcohol consumption is a risk factor for many chronic diseases, including some cancers, type 2 diabetes, and Alzheimer’s disease. Individuals with a specific variant of the aldehyde dehydrogenase 2 gene (ALDH2*2) are at higher risk of many of these diseases. Given that ALDH2*2 is the most common single genetic variation in humans and that more than half of all American adults drink alcohol, an opportunity is present for targeted chronic disease risk reduction in a large number of Americans. However, in order to design effective public health strategies, such as targeted intervention programs, a better understanding of current alcohol consumption behaviors and associated factors, overall and stratified by ALDH2 genotype, is needed. This study aims to characterize the alcohol consumption behaviors among participants in the All of Us Research Program and examine factors that may be related to the behaviors, overall and by ALDH2 genotype.

Project Purpose(s)

Population Health
Social / Behavioral
Ancestry

Scientific Approaches

We will analyze data from the All of Us Research Program database regarding alcohol consumption behaviors, ALDH2 genotype (rs671), demographics, personal and family health history, socioeconomic factors, lifestyle factors, and social determinants of health. All participants with informative data for rs671 will be included in the study. These data sets will be from surveys, physical measurements, and the genomic data set. Statistical analyses will be done using R or python. We will examine relationships between these factors and alcohol consumption using Fisher’s exact test (categorical variables) and the Kruskal-Wallis test (continuous variables), overall and stratified by ALDH2 genotype and potentially other factors, for example, demographics.

Anticipated Findings

We hypothesize that alcohol consumption behaviors will be associated with factors including demographics, personal and family health history, socioeconomic factors, lifestyle factors, and social determinants of health, with ALDH2 genotype and potentially other factors. While a limited number of U.S. studies among university students have shown that ALDH2*2 homozygotes tend to avoid alcohol, many ALDH2 heterozygotes do consume alcohol, albeit at lower levels. There have been no studies examining alcohol consumption behaviors by ALDH2 genotype conducted outside the university setting in the U.S. The All of Us Research Program presents a valuable source of data from study participants across the U.S. which would enable the study of alcohol consumption behaviors in the context of genomics.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Manxi Yang - Graduate Trainee, University of California, Irvine
Hannah Park - Other, University of California, Irvine
Danielle Forman - Other, University of California, Irvine

Collaborators:

Jacqueline Kim - Other, University of California, Irvine
Hester Nguyen - Undergraduate Student, University of California, Irvine

Monsivais Lab endometriosis

We hope to explore the social determents of health that relate to endometriosis and early pregnancy loss. We will look at age, ethnicity, access to education, and other demographics to describe correlations with our disease of interest. By studying this…

Scientific Questions Being Studied

We hope to explore the social determents of health that relate to endometriosis and early pregnancy loss. We will look at age, ethnicity, access to education, and other demographics to describe correlations with our disease of interest. By studying this topic, we hope to improve understanding of environmental factors that impact these two common conditions.

Project Purpose(s)

Disease Focused Research (endometriosis, miscarriages)

Scientific Approaches

We will create cohorts of women with endometriosis and women with recurrent miscarriages. We will then analyze different SDOH within our cohorts. Cases will be compared to controls using Pearson’s Chi-squared test or Fisher exact test for categorical variables and unpaired t-test for continuous variables. Multivariable models will be built using logistic regression, taking into account universal cofounders (e.g. age, sex), a priori associations, and co-variates with significance of P <0.1 in univariate analysis, followed by backward elimination of co-variates with a significance of P >0.1 or with evidence of collinearity.

Anticipated Findings

We hope to elucidate environmental contributing factors to endometriosis or recurrent miscarriages. This would allow physicians to better educate patients and provide care.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Dominique Cope - Graduate Trainee, Baylor College of Medicine
Diana Monsivais - Early Career Tenure-track Researcher, Baylor College of Medicine

Collaborators:

Juliet Alexander - Graduate Trainee, Baylor College of Medicine

Endometriosis 1

I plan to investigate Endometriosis and how it relates to women's physical and mental health. We are hoping to see how these effects vary among racial, ethnic, and other social lines. Additionally, we plan to investigate the use of different…

Scientific Questions Being Studied

I plan to investigate Endometriosis and how it relates to women's physical and mental health. We are hoping to see how these effects vary among racial, ethnic, and other social lines. Additionally, we plan to investigate the use of different interventions and therapies.

Project Purpose(s)

Disease Focused Research (endometriosis)

Scientific Approaches

We will be using a control group and experimental group to investigate Endometriosis and look at rates of anxiety and depression within these patient populations.

Anticipated Findings

We are hoping to investigate how anxiety and depression vary among different population with Endometriosis and how different populations use therapies. There is literature regarding worse mental and physical health for women with Endometriosis; however, there are few studies that look at how these vary among different populations of women. Also, comparing how therapies are used between population would be novel.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Juliet Alexander - Graduate Trainee, Baylor College of Medicine

Collaborators:

Diana Monsivais - Early Career Tenure-track Researcher, Baylor College of Medicine

ALDH2 PheWAS

One of the most important genes for alcohol metabolizing enzymes is ALDH2. People who have harmful mutations in the ALDH2 gene have an alcohol flush reaction in response to drinking alcohol. This flushing reaction is also associated with an increased…

Scientific Questions Being Studied

One of the most important genes for alcohol metabolizing enzymes is ALDH2. People who have harmful mutations in the ALDH2 gene have an alcohol flush reaction in response to drinking alcohol. This flushing reaction is also associated with an increased likelihood of developing certain cancers. Variants in the ALDH2 gene are common in people from East Asia, however, variants can be observed in people of all ethnicities.
Several East Asian cohort studies have looked at associations between specific variants in ALDH2 and health outcomes such as hypertension and cancer. To date, no study has examined variants in ALDH2 across ethnicities in a broad cohort or their effect on health outcomes. This study seeks to examine phenotypic associations of ALDH2 in a diverse cohort. By establishing a PheWAS workflow we hope to determine phenotypic associations of several ALDH2 variants.

Project Purpose(s)

Ancestry

Scientific Approaches

I plan to utilize a phenotype-wide association study to examine the phenotypes associated with different ALDH2 variants in a diverse population.

A recent study developed a PheWAS tool that is freely available through the Python package index and can be run virtually through the ‘All of Us’ platform called PheTK (Tran et al. 2024). I plan to utilize this PheTK tool to determine what phenotypes drawn from the electronic medical record are associated with several ALDH2 variants.

Tran, T. C., Schlueter, D. J., Zeng, C., Mo, H., Carroll, R. J., & Denny, J. C. (2024). PheWAS analysis on large-scale biobank data with PheTK. medRxiv : the preprint server for health sciences, 2024.02.12.24302720. https://doi.org/10.1101/2024.02.12.24302720

Anticipated Findings

Various studies looking at the impact of ALDH2 have been performed on specific East Asian populations, however, a study has not been conducted that integrates a diverse dataset from people of multiple ethnic backgrounds to determine the phenotypic effects of ALDH2 mutations.

We anticipate that we will find some similar associations to previously studied east asian cohorts, showing an increase in esophageal cancer, osteoporosis, heart disease, and hypertension. ALDH2 also metabolizes several aldehyde amines of neurotransmitters, thus it could be implicated in psychiatric and neurocognitive disorders. We could find associations with disorders such as Alzheimer's, Parkinson's, anxiety, or panic disorders. Preliminary evidence also implicates ALDH2 variants for increased risks for diabetes, which could be seen as an associated phenotype within this study.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Emiko Waight - Graduate Trainee, Northwestern University

Capstone Test

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Scientific Questions Being Studied

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Project Purpose(s)

Disease Focused Research (cancer)

Scientific Approaches

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Anticipated Findings

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Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Saul Ashley - Graduate Trainee, Meharry Medical College

“Version 6 Exploring the All of Us data for hypertension and other phenotypes”

“Not applicable – this workspace is intended for educational purposes for the 2024 BR Faculty Summit. We are learning how to use the Researcher Workbench by analyzing data for hypertension and other phenotypes.” “We are also replicating work of a…

Scientific Questions Being Studied

“Not applicable – this workspace is intended for educational purposes for the 2024 BR Faculty Summit. We are learning how to use the Researcher Workbench by analyzing data for hypertension and other phenotypes.”

“We are also replicating work of a previous study on hypertension prevalence across geographic states by Chandler et al. 2021 that requires us to use v6 of the data.”

Project Purpose(s)

Educational

Scientific Approaches

“Not applicable – this workspace is intended for educational purposes for the 2024 BR Faculty Summit. We are learning how to use the Researcher Workbench by analyzing data for hypertension and other phenotypes.”

“We are also replicating work of a previous study on hypertension prevalence across geographic states by Chandler et al. 2021 that requires us to use v6 of the data.”

Anticipated Findings

“Not applicable – this workspace is intended for educational purposes for the 2024 BR Faculty Summit. We are learning how to use the Researcher Workbench by analyzing data for hypertension and other phenotypes.”

“We are also replicating work of a previous study on hypertension prevalence across geographic states by Chandler et al. 2021 that requires us to use v6 of the data.”

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Maria Alejandra Gonzalez Gonzalez - Research Fellow, Baylor College of Medicine

Strategies for associating rare coding & non-coding variants with common disease

Most genetic studies have focused on the small percent of variants that are present in at least ~1% of the population. This is both because it is easier to measure a limited number of variants than to measure a whole…

Scientific Questions Being Studied

Most genetic studies have focused on the small percent of variants that are present in at least ~1% of the population. This is both because it is easier to measure a limited number of variants than to measure a whole genome, and because fewer people are needed to find statistically robust associations with common variants than with rare ones. An increasing number of studies perform whole-exome sequencing, but only for the ~2% of the genome that encodes proteins. Whole-genome sequencing (WGS) does allow ascertainment of rare non-protein-coding variants.

Overlooking almost all of the variants in the genome is a substantial research gap; therefore, we aim to use WGS data to find associations between rare non-protein-coding variants and a wide spectrum of diseases, in a "phenome-wide" manner. We will use Bayesian approaches to perform grouped association testing of rare variants. Lastly, we will develop a region-based test for survival outcomes for this project.

Project Purpose(s)

Methods Development
Ancestry

Scientific Approaches

We aim to find associations between rare non-protein-coding variants and a wide spectrum of diseases, in a "phenome-wide" manner (i.e. not restricting ourselves to a particular phenotype or class of phenotypes). This study design is often called a phenome-wide association study (PheWAS). Rare non-protein-coding variants are especially challenging to test for disease associations. Most are too rare to study individually, meaning they need to be grouped together for association testing. But being non-protein-coding, they lack an obvious functional unit (like a gene) to provide a scaffold for this grouping. We will leverage extensive work on Bayesian approaches for region-based association testing of rare variants which overcomes both of these limitations. We will also include survival phenotypes in our analysis, and region-level genetic association methods that account for local linkage disequilibrium. A region-based test for survival outcomes will also be developed for this project.

Anticipated Findings

We aim to link a highly understudied class of genetic variants with human disease and provide the results as a resource to the biomedical research community to be able to perform follow-up experimental characterization. In particular, we aim to identify “pleiotropic” variants that are associated with multiple phenotypes. Such variants may shed light on shared genetic pathways across diseases and point to transdiagnostic therapies and drug repurposing opportunities. In line with the spirit of open data, we will make the PheWAS results available to the biomedical research community through an interactive web application, as well as through the GWAS Catalog (https://www.ebi.ac.uk/gwas) for researchers interested in the full results.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Isabel Kerrebijn - Graduate Trainee, Sinai Health System

Mental Health

What factors show a higher correlation with worse mental health symptoms? Based on that information, what interventions can be done and how can they be optimized?

Scientific Questions Being Studied

What factors show a higher correlation with worse mental health symptoms? Based on that information, what interventions can be done and how can they be optimized?

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We'll be using machine learning techniques to study electronic health records.

Anticipated Findings

We're hoping to find the the factors that correlate with worse mental illness symptoms. Using this information, we'll design scheduling systems to maximize efficiency and impact

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Kavya Velmurugan - Graduate Trainee, Georgia Institute of Technology

Alzheimer’s Disease Data Builder and Prediction Model

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease (AD). Treatments of AD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important…

Scientific Questions Being Studied

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease (AD). Treatments of AD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important because it will help improve the early diagnosis of high-risk patients and the preventive care and interventions that follow.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Methods Development

Scientific Approaches

The datasets that we will use include the electronic health records (EHR) data for patients with Alzheimer’s disease and related dementias. We will also attempt to find other data modalities that can be integrated with EHR to improve the performance of our predictor.

Anticipated Findings

There is a complex relationship among different biomedical data modalities and by finding a bridge to connect these data, we can create predictive models of AD that are highly scalable, efficient, accurate, and interpretable.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

qiannan zhang - Research Fellow, Cornell University
Haoxuan Zhang - Graduate Trainee, Cornell University

Collaborators:

Wenxin Chen - Graduate Trainee, Cornell University
Taykhoom Dalal - Graduate Trainee, Cornell University

Evaluations of ADRD

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease and related Dementia (ADRD). Treatments of ADRD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s…

Scientific Questions Being Studied

Our study focuses on using machine learning to build an early predictive model of Alzheimer’s Disease and related Dementia (ADRD). Treatments of ADRD patients often fail due to the too-late administration of clinical intervention. Therefore, the early prediction of Alzheimer’s Disease is important because it will help improve the early diagnosis of high-risk patients and the preventive care and interventions that follow.

Project Purpose(s)

Disease Focused Research (Alzheimer’s Disease and Related Dementia)
Population Health
Control Set

Scientific Approaches

The datasets that we will use include the electronic health records (EHR) data for patients with Alzheimer’s disease and related dementias. We will also attempt to find other data modalities that can be integrated with EHR to improve the performance of our predictor.

Anticipated Findings

There is a complex relationship among different biomedical data modalities and by finding a bridge to connect these data, we can create predictive models of AD that are highly scalable, efficient, accurate, and interpretable.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

qiannan zhang - Research Fellow, Cornell University

Duplicate of How to Work with All of Us Survey Data (v7)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? By running the notebooks in this workspace, you should get familiar with how to query…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect?
By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.

Project Purpose(s)

Educational
Methods Development
Other Purpose (This is an All of Us Tutorial Workspace created by the Researcher Workbench Support team. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, researchers will learn the following:
- how to query the survey data,
- how to summarize PPI modules, and questions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Ruizhi Huang - Project Personnel, Saint Louis University

predictive models for OUD

To objective of this study is to develop predictive models using machine learning that will integrate clinical, social, genomic, and demographic features to identify patients that are higher risk for opioid use disorder. This is important because we need to…

Scientific Questions Being Studied

To objective of this study is to develop predictive models using machine learning that will integrate clinical, social, genomic, and demographic features to identify patients that are higher risk for opioid use disorder. This is important because we need to better identify patients at risk in order to improve how we allocate resources to those who are prescribed opioids in order to reduce incidence of opioid addiction.

Project Purpose(s)

Disease Focused Research (opioid use disorder)
Social / Behavioral
Ancestry

Scientific Approaches

We will use various machine learning approaches (e.g., deep learning, foundation models) to identify patients at risk for opioid use disorder. This will involve creating a cohort of all patients prescribed an opioid during their case. The population will be split into those who had or did not have a diagnosis of opioid use disorder (e.g., ICD10 F11.xx). Predictor variables that will be included are responses to survey questions (e.g., social determinants of health), demographic/geographic data, diagnosis codes, procedure codes, medications, and genomic information. These models will include genomic information from SNPs as well as markers discovered via GWAS. We will train models with a portion of the dataset and will validate the models on a separate test set.

Anticipated Findings

We anticipate that we can generate predictive models for opioid use disorder among patients prescribed an opioid, have chronic pain, and/or underwent surgery. This may potentially provide clinicians a tool to identify which of their patients are at high risk of addiction prior to prescribing opioids for pain.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Rodney Gabriel - Early Career Tenure-track Researcher, University of California, San Diego

Collaborators:

Onkar Litake - Graduate Trainee, University of California, San Diego

Antidepressant Project

Examining the duration of antidepressant usage and reasons for discontinuation provides critical insights into the efficacy of antidepressant treatment among individuals coping with depression. This analysis is imperative for refining treatment strategies and enhancing mental health outcomes on a broader…

Scientific Questions Being Studied

Examining the duration of antidepressant usage and reasons for discontinuation provides critical insights into the efficacy of antidepressant treatment among individuals coping with depression. This analysis is imperative for refining treatment strategies and enhancing mental health outcomes on a broader scale. Given the widespread utilization of antidepressants, their impact transcends individual patients and significantly influences public health. Delving into usage patterns and associated comorbidities, specifically among African Americans, enables the identification of targeted interventions aimed at improving population-level mental health outcomes.

Project Purpose(s)

Educational

Scientific Approaches

To address our research inquiries effectively, we intend to employ a rigorous scientific framework. Multiple datasets would be utilized to gather comprehensive information pertinent to the study, which include patient demographics, details on deceased individuals, data on antidepressant usage, and information on various diseases, each providing essential insights into the research objectives. The research methodology will involve descriptive analyses to characterize the study population, assess antidepressant usage patterns, and profile comorbidities. Additionally, statistical modeling techniques would be employed to evaluate the fit between observed data and expected outcomes, thereby assessing the validity of the research hypotheses. To facilitate the research endeavors, sophisticated tools and technologies would be leveraged, specifically Python programming language.

Anticipated Findings

The investigation is poised to unveil insights into the antidepressant utilization within African American patient cohorts, shedding light on factors such as duration of use, adherence rates, and determinants of discontinuation. By examining prevalent comorbidities, it will identify common health challenges within this demographic, and may uncover key determinants influencing antidepressant usage, such as socioeconomic status, cultural beliefs, and healthcare provider practices. A nuanced understanding of these determinants and the resultant findings are poised to carry substantial public health implications, informing the development of policies and initiatives aimed at mitigating mental health disparities and fostering psychological well-being within African American populations.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Yatisha Rajanala - Graduate Trainee, George Mason University

Common microdeletion and duplication syndromes – stage 1

This study aims to identify genetic modifiers influencing the penetrance and variable expression of common microdeletion and duplication syndromes, such as 15q11.2 deletion, 16p11.2 duplication and 22q11.2 duplication. Common microdeletions/duplications, such as 15q11.2 deletion, 16p11.2 duplication and 22q11.2 duplication, are…

Scientific Questions Being Studied

This study aims to identify genetic modifiers influencing the penetrance and variable expression of common microdeletion and duplication syndromes, such as 15q11.2 deletion, 16p11.2 duplication and 22q11.2 duplication.

Common microdeletions/duplications, such as 15q11.2 deletion, 16p11.2 duplication and 22q11.2 duplication, are cytogenetic abnormalities frequently identified in patients with neurodevelopmental and psychiatric phenotypes. However, the estimated penetrance of these syndromes is low, and the underlying mechanism behind this remains unknown. We propose this study to identify genetic variants contributing to the expressivity of these common microdeletion and duplication syndromes.

Project Purpose(s)

Disease Focused Research (Common microdeletion and duplication syndromes)
Ancestry

Scientific Approaches

Cohort: A cohort of individuals with common microdeletions/duplications will be generated.

Datasets: chromosomal microarray data, whole genome-sequencing data, and phenotype data will be utilized.

Method: Genome-wide association (GWA) analysis will be conducted using genetic and phenotype data to identify genetic variants associate with the penetrance of these syndromes. Additionally, we will generate polygenic risk score (PRS) and assess the potential contribution to the variable expression of these syndromes.

Computation tools: Hail, PLINK, Python, and R.

Anticipated Findings

Through this study, we expected to identify genetic modifiers that contribute to the penetrance and variable expression of these common microdeletion and duplication syndromes. The findings from our research will aid in disease stratification and deepen our understanding of the mechanism of these common microdeletion and duplication syndromes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Nifang Niu - Other, University of Iowa

Duplicate of CMS_Disparities in maternal mortality and morbidity in the USA]

Maternal health is an important part of the health system of any country. Wit U.S, maternal mortality and morbidity is higher compared to any other developed country. According to PMSS report maternal death rate is 17.3 per 100,000 live birth.…

Scientific Questions Being Studied

Maternal health is an important part of the health system of any country. Wit U.S, maternal mortality and morbidity is higher compared to any other developed country. According to PMSS report maternal death rate is 17.3 per 100,000 live birth. The World Health Organization (WHO) report says the position of the U.S in maternal mortality ranking is 56, which is unacceptable for a developed country. A clear picture of disparity is present in every report dealing this topic. The mortality rate among black American women is about 3 times higher than Non-Hispanic white women. The death rate among other minorities like Non-Hispanic American Indian or Alaska Native, Asian-Pacific Islander is also higher. The case of maternal morbidity is also not different. The maternal death rate among Hispanic-Whites are lower, however Severe Maternal Morbidity (SMM) is higher among this minority group.

Project Purpose(s)

Population Health
Social / Behavioral
Educational

Scientific Approaches

With the time-stamped data for different procedures, laboratory results, and other hospital visits for the patient cohort, we aim to develop a process mining algorithm to identify variations in care pathways that cause adverse maternal outcomes. Process mining approaches in healthcare to identify variability in system level factors is a newer approach to conduct disparity research. Our research will address this gap in literature.
We hope to address the potential stigmatization issues by educating necessary stakeholders including hospital, providers, and policymakers. Once we have a preliminary framework, we hope to conduct a community based participatory research and engage with the community members. We propose that the process mining approach would help providers identify the “hotspots” in the care pathways that cause disparities.

Anticipated Findings

The major factors causing maternal mortality and morbidity are sociodemographic, socioeconomic, provider factors and system level factors. This research investigates the system level factors that can cause disparity in maternal health. With the AllofUs data we are trying to group the women utilized the healthcare system for their maternal care, with respect to their race/ethnicity, pregnancy complications, outcome etc. and find out the factors that caused adverse pregnancy outcome, mortality, and morbidity. Moreover, we apply novel process mining approaches to map the patient cohort and identify any changes in care pathways that may result in disparities.

The research will be helpful to find out the system-level factors other than income, insurance, or social status causing disparity in maternal health. Also, it can help in reducing those factors that have a major role in maternal health care disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Hala Al Sliti - Graduate Trainee, Binghamton University

Collaborators:

Ashaar Rasheed - Graduate Trainee, Binghamton University

Identification of common gene loci between AD and IBD patients

Our Genome-Wide Association Study (GWAS) on atopic dermatitis (AD) and inflammatory bowel disease (IBD) aims to uncover a possible genetic basis explaining the known association between AD and IBD. This research will increase understanding of common molecular pathways influencing disease…

Scientific Questions Being Studied

Our Genome-Wide Association Study (GWAS) on atopic dermatitis (AD) and inflammatory bowel disease (IBD) aims to uncover a possible genetic basis explaining the known association between AD and IBD. This research will increase understanding of common molecular pathways influencing disease development and allow providers to personalize medical interventions and treatments for their patients.

Project Purpose(s)

Ancestry

Scientific Approaches

We aim to utilize raw datasets containing whole-genome sequencing profiles from patients diagnosed with atopic dermatitis and inflammatory bowel diseases, such as Crohn’s and Ulcerative Colitis. Analysis tools we aim to utilize for this study include association tests like chi-square or logistical regressions to assess the association between genetic markers (SNPs) and the presence of atopic dermatitis and IBD. Gene Based testing will also be utilized to aggregate information across multiple SNPs within individual genes and perform gene-based association tests such as SKAT or MAGMA. These tests will assess whether the cumulative effect of genetic variation within a gene is associated with both atopic dermatitis and IBD.

Anticipated Findings

In comparing atopic dermatitis (AD) and inflammatory bowel disease (IBD), our goal is to identify shared genes and uncover potential genetic links between these conditions. Anticipated findings include specific genetic markers associated with both diseases. This insight may reveal common pathways contributing to disease development and allow for more targeted and personalized medical interventions. The integration of genetic data into healthcare empowers providers to educate patients about their genetic risks, enabling proactive disease management and optimized treatment outcomes. This includes offering personalized guidance on lifestyle choices, environmental factors, and treatment options that may affect outcomes. This proactive approach has the potential to significantly enhance disease prevention and management.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Rachael Cowan - Graduate Trainee, University of Alabama at Birmingham
Hanna Terhaar - Graduate Trainee, University of Alabama at Birmingham

IE565

Want to determine the factors affecting Asthma. Want to look on both the medial and social factors through the use of Social determinants of health surveys

Scientific Questions Being Studied

Want to determine the factors affecting Asthma. Want to look on both the medial and social factors through the use of Social determinants of health surveys

Project Purpose(s)

Educational

Scientific Approaches

I want to us Machine Learning approaches to study the effects. Also will use different statistical measures for analysis.

Anticipated Findings

Contributing factors for asthma. Also different social factors contributing to it, which I will analysis from surveys

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Dhiraj Pokhrel - Graduate Trainee, University of Tennessee, Knoxville

Impact of Demographic Factors on Tobacco, Alcohol, and Drug

Research Question: How do demographic factors influence tobacco, alcohol, and drug use among Americans? Importance: Public Health: Understanding demographics' impact on substance abuse aids in designing targeted interventions. Vulnerability Identification: Analysis of age, gender, ethnicity, and socioeconomic status helps identify…

Scientific Questions Being Studied

Research Question: How do demographic factors influence tobacco, alcohol, and drug use among Americans?
Importance:
Public Health: Understanding demographics' impact on substance abuse aids in designing targeted interventions.
Vulnerability Identification: Analysis of age, gender, ethnicity, and socioeconomic status helps identify at-risk groups.
Policy Formulation: Informed policies addressing social determinants of health can reduce substance abuse.
Equity Promotion: Policies targeting socioeconomic disparities promote equitable healthcare access.
Community Health: Insights into demographics and substance abuse foster healthier and fairer communities.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

The study will employ regression analyses using data from the Basics and Lifestyle surveys within the All of Us research program. These surveys provide comprehensive demographic, behavioral, and substance use data from a diverse sample of approximately one million U.S. participants. Demographic factors will serve as predictors, while substance use behaviors, including tobacco, alcohol, and drug use, will be assessed through survey questions. The analysis aims to elucidate the relationships between demographic variables and substance use patterns to inform public health interventions.

Anticipated Findings

The anticipated findings are expected to reveal associations between demographic factors and substance use behaviors, shedding light on the complexities of tobacco, alcohol, and drug use patterns among diverse populations. These findings would contribute significantly to the scientific understanding of how social determinants influence substance abuse, informing the development of targeted interventions and policies aimed at reducing disparities and improving public health outcomes. Additionally, the study's insights may highlight specific demographic groups at heightened risk, guiding more tailored prevention and treatment strategies to address the unique needs of vulnerable populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Promethi Das Deep - Graduate Trainee, Sam Houston State University

Collaborators:

Cindy Chen - Teacher/Instructor/Professor, Sam Houston State University

IGS_protein

Explore the utility of UK Biobank-trained omic genetic scores on an external cohort such as All of Us.

Scientific Questions Being Studied

Explore the utility of UK Biobank-trained omic genetic scores on an external cohort such as All of Us.

Project Purpose(s)

Methods Development

Scientific Approaches

The dataset that was used for generating the weights of omic genetic score is the UK Biobank dataset. The tool/model that was used for generating the weights is PRS-CS.

Anticipated Findings

We hope to replicate disease stratification patterns observed on the UKB replication set on an external dataset, which would be evidence of the generalizability of omic genetic scores.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xiaochen Yang - Graduate Trainee, University of Pennsylvania

Orthopedic fractures

Examine the effect of orthopedic fractures on the well being of patients across various socioeconomic axis.

Scientific Questions Being Studied

Examine the effect of orthopedic fractures on the well being of patients across various socioeconomic axis.

Project Purpose(s)

Disease Focused Research (orthopedic fractures)

Scientific Approaches

We intend to create a dataset of orthopedic fractures that and examine the socioeconomic impact that exists amongst the group to assess discrepancy in care that might currently exist in the medical system

Anticipated Findings

The findings could indicate strategies to reduce the overall burden of health affects on these populations

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Lance Lamore - Graduate Trainee, California University of Science and Medicine

Collaborators:

Alejandro Schmieder - Project Personnel, Stanford University

Antidepressant project

This project is for my coursework in EHR analysis at GMU, I will look into antidepressant success rates for African Americans.

Scientific Questions Being Studied

This project is for my coursework in EHR analysis at GMU, I will look into antidepressant success rates for African Americans.

Project Purpose(s)

Educational

Scientific Approaches

Use Python and SQL to check the likelihood of remission from various antidepressants given patient characteristics.

Anticipated Findings

See how antidepressants affect African Americans compared to their baseline.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Nahida Farheen Shaik - Student, George Mason University

F31

I aim to explore the prevalence and correlates of post partum depression among gender minority people.

Scientific Questions Being Studied

I aim to explore the prevalence and correlates of post partum depression among gender minority people.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

I plan to use descriptive statistical methods and potentially structural equation modeling to understand the prevalence of postpartum depression among gender minority people and various risk factors associated with PPD.

Anticipated Findings

I expect that postpartum depression rates are higher among gender minority birthing people compared to national average rates of PPD. These findings could inform gender affirming healthcare providers about the importance of postpartum check ups for this particular population.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Pond Ezra - Graduate Trainee, University of Maryland, College Park

Secondary Project Registered Tier

We aim to determine the prevalence and risk factors of chronic pain among adults in the US. While there is evidence that social determinants are critical determinants of pain experience and outcomes, there are still large gaps in knowledge regarding…

Scientific Questions Being Studied

We aim to determine the prevalence and risk factors of chronic pain among adults in the US. While there is evidence that social determinants are critical determinants of pain experience and outcomes, there are still large gaps in knowledge regarding the role social determinants may play in chronic pain sub-populations. That is, it is relatively unknown whether demographic and psychosocial risk factors differ by etiology of pain. This project has two aims: Aim 1: identify the prevalence of chronic pain and specific pain etiologies overall and in subpopulations determined by demographic, geographic, and SES; Aim 2: Assess whether social determinants and lifestyle factors are associated with specific pain diagnoses. A better understanding of the distribution and determinants of pain by pain diagnoses may elucidate patterns specific to pain etiology or pain generally and may lend insights into general and specific drivers of pain prevalence in a diverse population.

Project Purpose(s)

Educational

Scientific Approaches

Our approach will use traditional epidemiological and statistical methods. Demographic, geographic, socioeconomic, health characteristics, social determinants, lifestyle factors, and pain characteristics will be described and visualized using means, standard deviations, confidence intervals, frequencies, and percentages. Care will be taken to test assumptions, assess outliers, and select the most appropriate analytical test. Based on the distribution we will test differences between subgroups and test associations with the appropriate test, for example, multivariable logistic regression or nonparametric equivalent. If there is sufficient evidence supporting the hypothesized main effect, interactions will also be tested. We will also correct for multiple comparisons. The datasets of interest include survey questions (the basics, overall health, lifestyle, health care access and utilization, personal health history, social determinants of health), and EHR domains (condition).

Anticipated Findings

A major gap in the literature and an aim of this proposed project is to describe the prevalence, distribution, and determinants related to specific pain conditions in a diverse sample. This is descriptive in nature and will elucidate patterns that may inform future hypothesis-based questions about the characteristics driving chronic pain and chronic pain outcomes. This proposed descriptive analysis can add substantial insight into, currently unknown, patterns of pain and how this may differ by pain diagnosis.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Sindhu Karnam - Graduate Trainee, University of Pittsburgh
Kailyn Witonsky - Graduate Trainee, University of Pittsburgh
Chen Hu - Graduate Trainee, University of Pittsburgh

Melanoma and autoimmunity

We are looking to explore the risk of autoimmunity and other various inflammatory conditions associated with melanoma.

Scientific Questions Being Studied

We are looking to explore the risk of autoimmunity and other various inflammatory conditions associated with melanoma.

Project Purpose(s)

Disease Focused Research (Melanoma)

Scientific Approaches

We plan to use retrospect cohort study or case control, depending on what data is available, in a multivariable model to assess significance.

Anticipated Findings

We anticipate several significant relationships between inflammatory or autoimmune conditions and the development of melanoma.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Harrison Zhu - Graduate Trainee, Baylor College of Medicine

NEW Pregnancy and DR

We are looking at exploring eye conditions and eye exam frequencies in pregnant populations, examining demographic factors (race, age) and socioeconomic risk factors (highest education level, yearly income) and how they correlate to various pregnant diagnoses (such as diabetes, hypertension,…

Scientific Questions Being Studied

We are looking at exploring eye conditions and eye exam frequencies in pregnant populations, examining demographic factors (race, age) and socioeconomic risk factors (highest education level, yearly income) and how they correlate to various pregnant diagnoses (such as diabetes, hypertension, preeclampsia, etc.)

Project Purpose(s)

Disease Focused Research (diabetic retinopathy)

Scientific Approaches

We aim to use first and second trimester pregnancy diagnoses and measure eye exams coded for within 6-9 months after those diagnoses, respectively to define pregnancy period. Additionally, we aim to use the demographic information and income and education data found in AoU's "The Basics" Survey.

Anticipated Findings

We anticipate that there may be differences in eye exam screening rates according to different associated disease prevalence as well as different demographic and socioeconomic status factors.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Michelle Ko - Graduate Trainee, University of California, Los Angeles

Collaborators:

Ramin Talebi - Graduate Trainee, University of California, Los Angeles

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