Research Projects Directory

SM APOE 2024

Variants in the Apolipoprotein E (APOE) have been shown to have differential risk for conditions such as Alzheimer's disease and cardiovascular disease. This workspace will assess the feasibility of a large APOE association study by summarizing the APOE genotypes, hypercholesterolemia…

Scientific Questions Being Studied

Variants in the Apolipoprotein E (APOE) have been shown to have differential risk for conditions such as Alzheimer's disease and cardiovascular disease. This workspace will assess the feasibility of a large APOE association study by summarizing the APOE genotypes, hypercholesterolemia and demographics.

Project Purpose(s)

Educational
Ancestry

Scientific Approaches

The two rsIDs for APOE genotyping (rs429358 and rs7412) will be extracted from AllOfUs Hail tables. Data will be summarized into the defined APOE genotypes (ε2, ε3, ε4) and associated with hypercholesterolemia status. Hypercholesterolemia will be defined by the SNOMED code "E78.0 - Pure hypercholesterolemia". Summary statistics for APOE association will be stratified by age, sex and declared race/ethnicity.

Anticipated Findings

The study hopes to find similar statics as has been found in the UKBiobank cohort, with the addition of higher diversity. The summary statistics of these findings will be used as preliminary data to propose a larger cohort study.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sevda Molani - Research Fellow, Institute for Systems Biology

Jing_Duplicate Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

JIng Zhang - Research Fellow, University of Kentucky

Duplicate of Alzheimers_GWAS_Take5

What are the genetic determinants of Alzheimer's disease in the All of Us cohort? Can we use this data to further explain what predisposes individuals to the disease?

Scientific Questions Being Studied

What are the genetic determinants of Alzheimer's disease in the All of Us cohort? Can we use this data to further explain what predisposes individuals to the disease?

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

I will produce a genome wide association study using Regenie, after data curation using Plink. I will conduct this in a mixed-ancestry population to maximize initial statistical power before considering stratified analyses.

Anticipated Findings

I expect to reproduce some existing AD GWAS findings, demonstrating how they have relevance to a more diverse cohort.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Julian Willett - Research Fellow, Broad Institute
Julian Willett - Research Fellow, Mass General Brigham
Dmitry Prokopenko - Other, Mass General Brigham

Collaborators:

Jun Qian - Other, All of Us Program Operational Use

Duplicate of ALDH2

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

Sang's Duplicate of Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yucong Sang - Project Personnel, University of Kentucky
Xian Wu - Research Fellow, University of Kentucky
Inori Tsuchiya - Project Personnel, University of Kentucky

Duplicate of Sleeping Disease & Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Daoming Lyu - Research Fellow, Cornell University

Duplicate of Duplicate of Inori Workspace

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sydney Shafer - Graduate Trainee, University of Kentucky

Duplicate of Sleeping Disease and Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Manqi Zhou - Graduate Trainee, Cornell University
Chang Su - Teacher/Instructor/Professor, Cornell University

A-to-I editing

Adenosine deaminases that act on RNA (ADARs) are a class of enzymes that catalyze the conversion of adenosine to inosine via hydrolytic deamination. Inosine is similar to chemically to guanine, so these RNA editing events have the potential to alter…

Scientific Questions Being Studied

Adenosine deaminases that act on RNA (ADARs) are a class of enzymes that catalyze the conversion of adenosine to inosine via hydrolytic deamination. Inosine is similar to chemically to guanine, so these RNA editing events have the potential to alter transcriptomic/proteomic function. I seek to explore data relevant to better understand A-to-I editing events in patients with Alzheimer's and Parkinson's disease, as abnormal RNA editing events have been implicated in various neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease.

Project Purpose(s)

Disease Focused Research (Parkinson's disease, Alzheimer's Disease)
Ancestry

Scientific Approaches

The dataset will contain genomic and transcriptomic data from patients diagnosed with Alzheimer's and Parkinson's diseases, as well as from healthy individuals. Such a dataset can serve as a comprehensive resource for examining A-to-I editing patterns. As this project is exploratory in nature, the primary goal is the development and utilization of a bioinformatic pipeline for the identification of patterns in A-to-I editing. Any findings will be used to inform development of experimental models to further investigate the role A-to-I editing plays with respect to neurodegenerative disease using Caenorhabditis elegans as a model organism.

Anticipated Findings

I anticipate discovering distinct patterns of A-to-I RNA editing in Alzheimer’s and Parkinson’s disease tissues compared to controls. These findings could include identification of specific sites where A-to-I editing is either lost or gained in the disease state and its correlation with disease severity, insight into the molecular mechanisms by which alterations in A-to-I editing contribute to the pathophysiology of these neurodegenerative diseases, and genetic variations which may have relevance to A-to-I editing with respect to Alzheimer's and Parkinson's.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Aidan Meyers - Undergraduate Student, University of Alabama

Sleeping Disease & Alzheimer's Disease

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients…

Scientific Questions Being Studied

Alzheimer's Disease is the most common form of dementia in the United States. This study means to identify phenotypic associations between AD & SD using machine learning (ML). The objective is to identify risk factors of mid-age and aging patients with SD to develop AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's Disease)

Scientific Approaches

We will use patient data within the observation window to build prediction model. We plan to use ML models including logistic regression, decision tree, and the advanced tree-based model XGboost. Data will be split into training and testing sets. Model hyperparameters will be tuned using a cross validation strategy in the training set.

Anticipated Findings

Based on the proposed study design, we expect to identify the specific phenotypic associations between Sleep Disorders (SD) and Alzheimer's Disease (AD) through the use of Machine Learning (ML) techniques. This will involve identifying various risk factors associated with mid-aged and older patients with SD that may predispose them to developing AD. Our models should illuminate individual-level outcomes for patients with AD, drawing on a range of structured patient records including demographic details, symptom profiles, comorbid conditions, medications, and Social Determinants of Health (SDoH). The outcomes from this study would not only deepen our understanding of the association between SD and AD but also provide practical tools and insights to help mitigate the impact of Alzheimer's disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Chang Su - Early Career Tenure-track Researcher, Temple University
Manqi Zhou - Graduate Trainee, Cornell University
Daoming Lyu - Research Fellow, Cornell University
Chang Su - Teacher/Instructor/Professor, Cornell University

AD project

I'm interested in factors that trigger Alzheimer's Disease, which has been found a efficacious treatment.

Scientific Questions Being Studied

I'm interested in factors that trigger Alzheimer's Disease, which has been found a efficacious treatment.

Project Purpose(s)

Educational

Scientific Approaches

I'm planning to use multivariable regression analysis to explore factors that might trigger Alzheimer's Disease.

Anticipated Findings

I'm expecting to see an association between environmental factors and Alzheimer's Disease. These findings will contribute to the prediction and prevention of Alzheimer's Disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xinhui Yang - Graduate Trainee, Louisiana State University Health Sciences Center, New Orleans

UCSD/ Oslo Group

Non-invasive and low-cost tools for early detection of individuals at high risk for developing Alzheimer’s Disease (AD) are of crucial importance. While genetic studies (GWAS) exist, most focus on European populations, limiting their application to diverse groups. Given the Eurocentric…

Scientific Questions Being Studied

Non-invasive and low-cost tools for early detection of individuals at high risk for developing Alzheimer’s Disease (AD) are of crucial importance. While genetic studies (GWAS) exist, most focus on European populations, limiting their application to diverse groups. Given the Eurocentric biases in GWASs, PRSs are better at predicting AD risk for European ancestry as opposed to others. This research addresses this gap by developing a multimodal hazard score (MHS) that incorporates ethnically and genetically diverse populations. Our team established a successful polygenic hazard score (PHS) that predicts AD onset in European cohorts. Higher PHS predicted greater cognitive decline in CN, entorhinal cortex volume loss and predicted conversion from cognitively normal (CN), to mild cognitive impairment (MCI) to AD. This suggests its potential as a robust genetic risk indicator to be included in the MHS while also looking into diverse populations.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

Leveraging data from the different AD cohorts, we will conduct Cox proportional models to develop the MHS combining age, PHS, brain atrophy, biofluid-based data, and clinical outcomes to predict neurocognitive decline trajectory. This score will be able to predict AD progression and identify individuals at high risk of transitioning from cognitive normalcy to mild cognitive impairment (MCI) and eventually AD in a generalizable population. Additionally, we will also compute power calculations to estimate required clinical trial sample sizes after hypothetical enrichment using the MHS. Finally, we aim to replicate these findings in other diverse cohorts to ensure broader applicability.

Anticipated Findings

We believe this MHS surpasses single-modal and European data-driven models in predicting AD for diverse populations. This could pave the way for earlier intervention, more efficient research, and ultimately, improved outcomes for individuals at risk of AD.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Xin Wang - Project Personnel, University of California, San Diego
Iris Broce - Early Career Tenure-track Researcher, University of California, San Diego
Gisele Sanda - Project Personnel, University of California, San Diego

Collaborators:

Elise Koch - Research Fellow, University of Oslo

SGM and Dementia

Study Aims: Aim 1: To investigate the differences in Alzheimer's disease diagnosis and receipt of treatment between sex and gender minority (SGM) and non-SGM older populations. Significance: Early diagnosis and timely initiation of treatment are essential for patients living with…

Scientific Questions Being Studied

Study Aims:
Aim 1: To investigate the differences in Alzheimer's disease diagnosis and receipt of treatment between sex and gender minority (SGM) and non-SGM older populations.
Significance: Early diagnosis and timely initiation of treatment are essential for patients living with Alzheimer’s disease and related dementia. Understanding and addressing cognitive care disparities in SGM older populations represent primary measures toward delivering high-quality care and alleviating the disease burden for a group significantly impacted by this condition.

Project Purpose(s)

Population Health

Scientific Approaches

Our study sample will include participants aged 65 years and above with electronic health records. The primary dependent variables are the diagnosis of Alzheimer's disease and related dementia and pharmaceutical treatment for Alzheimer's disease and related dementia. The primary independent variables are sexual and gender identity. Other covariates include sociodemographic and health-related variables.
Using a cross-sectional study design, univariate, bivariate, and multivariate analyses will be performed to investigate the associations between the independent variables and dependent variables.

Anticipated Findings

We expect to uncover significant differences in dementia care between sexual and gender minority (SGM) older adults and their non-SGM counterparts. These differences will likely be shaped by factors such as the individual's social network and the cultural competence of their healthcare providers.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Zhigang Xie - Early Career Tenure-track Researcher, University of North Florida

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

New Alzheimer workspace

The role of APOE mutation plays in the progression of Alzheimer's given effects in lifestyle and genetics

Scientific Questions Being Studied

The role of APOE mutation plays in the progression of Alzheimer's given effects in lifestyle and genetics

Project Purpose(s)

Educational

Scientific Approaches

Using data provided from the ALL OF US database, assistance from TA's and ATA's from class room, and discuss the research with my group.

Anticipated Findings

APOE mutation progresses the effect of Alzheimers in lifestyle and genetics. Our information would help better understand the role of the APOE mutation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Juan Martinez - Undergraduate Student, Arizona State University

Collaborators:

Nathan Roue - Undergraduate Student, Arizona State University

PheWAS for Role of Infectious Agents in Alzheimer's Disease

We are interested in the relationship between infectious agents, particularly bacteria, and Alzheimer's Disease in the All of Us research dataset. Specific questions we will ask are: 1. What other phecodes are enriched in individuals with Alzheimer's Disease? What other…

Scientific Questions Being Studied

We are interested in the relationship between infectious agents, particularly bacteria, and Alzheimer's Disease in the All of Us research dataset. Specific questions we will ask are:

1. What other phecodes are enriched in individuals with Alzheimer's Disease? What other phecodes are enriched in individuals with specific infections, such as infection with Pseudomonas aeruginosa? What overlap is there between these enrichments?

2. Is there unexpected segregation of these infection agents in individuals with genetic variants which have been reported to be risk or protective variants in Alzheimer's Disease?

3. In what ways do electronic health records report dementias, infections, and delirium, and how is this present in the All of Us research dataset.

We hypothesize that there will be enrichment of delirium and dementia in individuals who have certain infections.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Ancestry

Scientific Approaches

We will use AllofUs srWGS data and HAIL in order to extract genotype information in python, along with PheWAS developed by Joshua Denny at Vanderbilt to find associations between certain genotypes and phenotypes in R. We will also use other health surveys, as well as a wide range of information about the participants in order to control for environmental and background genotypic variables. These include ancestry from PCA's as computed by AllOfUS as well as age, sex, and geographic data.

Anticipated Findings

We anticipate identifying disease risk associated with infectious agents. This information will help guide other research on linking infectious agents and Alzheimer's Disease and possible preventative measures that can be taken, or further insight into disease pathways that can be disrupted. Potentially, these findings may help inform preventative care for individuals who may be at higher risk of developing certain conditions. The detailed code of our workbench will be freely available to those who ask, and will serve as an excellent reference for future analyses.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Paul Valdmanis - Early Career Tenure-track Researcher, University of Washington
Eli Kaufman - Project Personnel, University of Washington
Evangelos Nizamis - Project Personnel, University of Washington

Duplicate of Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yucong Sang - Project Personnel, University of Kentucky
Xian Wu - Research Fellow, University of Kentucky
Sydney Shafer - Graduate Trainee, University of Kentucky
Jordan Brown - Teacher/Instructor/Professor, University of Kentucky
JIng Zhang - Research Fellow, University of Kentucky
Inori Tsuchiya - Project Personnel, University of Kentucky
Hady Sabra - Graduate Trainee, University of Kentucky

GWAS Analysis 4

The purpose of this study is to investigate the genetic underpinnings of Alzheimer’s disease and other dementia types within the dementia cohort of the All of Us dataset. This includes the examination of known genetic loci for deviations linked to…

Scientific Questions Being Studied

The purpose of this study is to investigate the genetic underpinnings of Alzheimer’s disease and other dementia types within the dementia cohort of the All of Us dataset. This includes the examination of known genetic loci for deviations linked to specific Alzheimer’s subtypes and the prediction of the effectiveness of medications based on the presence of specific genetic markers. This field of research is important for developing a better understanding of the genetic factors in dementia, potentially leading to more effective and personalized treatments

Project Purpose(s)

Educational

Scientific Approaches

The intention of this workspace is to utilize the dementia cohort from the All of Us dataset, to predict phenotypic differences dependent on dementia classification type, and allelic data. The analysis is in- tended to involve machine learning tools and statistical techniques such as deep learning models and logistic regression. Clustering algorithms will also be used to identify possible heterogeneity within the dementia cohort.

Anticipated Findings

Anticipated outcomes of this study include the identification of genetic variations associated with spe- cific subtypes of Alzheimer’s disease and insights into how genetic profiles influence the effectiveness of medications in dementia treatment and personalized medicine approaches in treating related conditions. Findings in these areas could open new avenues for research into Alzheimer’s disease and pave the way for more tailored approaches in clinical settings, enhancing the overall management of dementia.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Wesley Lo - Graduate Trainee, Worcester Polytechnic Institute

Collaborators:

Zheyang Wu - Late Career Tenured Researcher, Worcester Polytechnic Institute

Duplicate of G*E Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Social / Behavioral
Methods Development
Ancestry
Other Purpose (Try to duplicate the workspace for completing the analyses)

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xian Wu - Research Fellow, University of Kentucky

Rates of Alzheimer's Disease and comorbid hypertension among women vs men

To what degree are females with high blood pressure affected by Alzheimer’s Disease compared to males with high blood pressure who are affected by Alzheimer’s Disease? Research that quantifies the relationship between biological sex, blood pressure, and development of AD…

Scientific Questions Being Studied

To what degree are females with high blood pressure affected by Alzheimer’s Disease compared to males with high blood pressure who are affected by Alzheimer’s Disease?

Research that quantifies the relationship between biological sex, blood pressure, and development of AD is needed. Females succumb to AD at higher rates than their male counterparts, and elevated blood pressure among older females is more prevalent, but to what degree do these three converge? Identifying these trends may drive future research specifically aimed at treating and preventing AD in females in all life stages.

Project Purpose(s)

Educational

Scientific Approaches

Our group will utilize EHR as well as survey data available within All of Us to identify patients with a diagnosis of Alzheimer's Disease as well as history or present diagnosis of hypertension. Pending volume of subjects which meet these initial criteria, we will stratify this data by systolic blood pressure range in order to determine whether severity or longevity of hypertension can be associated with increased occurrence of AD for either females or male, and to what degree they may differ.

Anticipated Findings

We expect to identify trends in Alzheimer's Disease development among hypertensive males and females. Greater understanding of the conditions which increase AD risk to females may drive future work to develop targeted preventative or treatment protocols.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jamie Watson - Undergraduate Student, Arizona State University

Collaborators:

Somer Lang - Undergraduate Student, Arizona State University
Cora Minch - Undergraduate Student, Arizona State University

Gene-Environment Interactions in Alzheimer’s Disease

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yucong Sang - Project Personnel, University of Kentucky
Xian Wu - Research Fellow, University of Kentucky
Sydney Shafer - Graduate Trainee, University of Kentucky
Jordan Brown - Teacher/Instructor/Professor, University of Kentucky
JIng Zhang - Research Fellow, University of Kentucky
Inori Tsuchiya - Project Personnel, University of Kentucky
Hady Sabra - Graduate Trainee, University of Kentucky

Collaborators:

Noah Perry - Project Personnel, University of Kentucky

Gene-Environment Interactions in Alzheimer’s Disease_test

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Other Purpose (testing workspace for Gene-Environment Interactions in Alzheimer’s Disease workspace)

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Inori Tsuchiya - Project Personnel, University of Kentucky

Collaborators:

Noah Perry - Project Personnel, University of Kentucky

GxE Interaction for AD

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly…

Scientific Questions Being Studied

60-80% of late-onset Alzheimer’s disease (LOAD) risk is heritable. Both genetic and environmental factors are responsible for the development and progression of LOAD. Many LOAD susceptibility genes have been identified by genome-wide association studies (GWAS). While genetic factors contribute significantly to the risk of developing LOAD, the impact of environmental factors on the disease remains multifaceted. We propose a research proposal that assesses gene-environment (G×E) interactions in LOAD to analyze All of Us data. We will test these hypotheses: 1) individuals with the same or lower genetic risks will face an increased risk for LOAD when modified by higher environmental risks (vulnerability), whereas those with the same or higher genetic risks will experience a reduced risk when influenced by lower environmental risks (resilience); 2) effects of genetic risks on LOAD will vary modified by midlife and later-life environmental risks across ethnoracial groups.

Project Purpose(s)

Educational

Scientific Approaches

Polygenic risk scores (PRSs) are well-established in LOAD studies and feature an estimate of an individual’s genetic liability to LOAD by aggregating genetic effects of single-nucleotide variants (SNVs), thus serving as a comprehensive score for genetic risks. We will examine the interactions between PRSs and each of the environmental risk factors in LOAD. We will also use an item response theory (IRT)-based model to generate environmental risk scores (ERSs) and investigate the interactions between PRSs and midlife/later-life ERSs in LOAD across ethnoracial groups. We will utilize the All of Us genetic data and published GWAS summary statistics to construct LOAD PRSs and construct midlife and later-life ERSs based on various environmental indicators in the All of Us data, including social of determinants of health (e.g., education and incomes), physical conditions (e.g., hypertension, diabetes, and depression), and lifestyle (e.g., smoking, alcohol, and exercise) variables.

Anticipated Findings

Overall, we will examine one-by-one G×E interactions in LOAD, but also construct comprehensive scores, PRSs and midlife/later-life ERSs, for individuals across ethnoracial groups, collectively contributing to advancing our knowledge of G×E interactions on vulnerability and resilience to LOAD. Informed by data indicating an overall risk (PRS modified by ERS) for LOAD, this approach may enable clinicians and individuals to initiate disease screening and discuss life planning strategies. This study would also provide a crucial tool for informing the design and implementation of personalized therapeutic and preventative programs, enabling more precise and individualized approaches to the treatment and prevention of LOAD. This study not only enhances our understanding of progression of LOAD but also provides a foundation for developing targeted strategies to mitigate risk and bolster resilience, thereby advancing personalized interventions for diverse populations at different life stages.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sydney Shafer - Graduate Trainee, University of Kentucky

Characterization and genetics of periodontal disease

I am joining a researcher who is studying "A Knowledge and Awareness Assessment of Oral Health Practices and Perceptions in Patients who were exposed to Adverse Childhood Experiences". Many people have gone through childhood trauma that has carried over into…

Scientific Questions Being Studied

I am joining a researcher who is studying "A Knowledge and Awareness Assessment of Oral Health Practices and Perceptions in Patients who were exposed to Adverse Childhood Experiences". Many people have gone through childhood trauma that has carried over into adulthood. As she states, the long term goal of this project is to examine the association between adverse childhood experiences (ACE) and the utilization of oral health services by patients 18 and older.
I am also interested in the genetic variants that can predispose people to periodontitis and dental caries. We can take that information into account when describing the impact of ACE on dental outcomes in later life.
Another project is "A Comparative Analysis of Periodontitis Diagnosis and Cognitive Decline in Patients with and without Dementia". The long-term goal of this project is to determine and compare if patients who have periodontitis disproportionately receive a diagnosis of dementia or Alzheimer's disease.

Project Purpose(s)

Disease Focused Research (periodontitis, dental caries)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

We will use phenotypic data that pertains to dental disease, ACEs, and cognition. A characteristics table will be generated and contingency tables will be assessed using chi-square tests. Multivariable logistic regression analysis will be conducted to develop models that predict A. oral health practices and outcomes and B. dementia and/or Alzheimer's disease. These models will describe any factors in our study that significantly impact the outcomes we are studying.
We will use genetic data to perform a GWAS if there is enough data to give sufficient statistical power to detect a genetic effect. This will let us use principal components analysis as a data reduction technique to condition our predictive models on genetic susceptibility to dental disease. Or, we may be able to identify genetic variants that predispose to periodontitis, allowing for precision intervention.

Anticipated Findings

As a health disparities researcher, I expect that patients on Medicaid and Black patients, independently of income level, are going to have experienced higher rates of ACEs and also higher rates of periodontitis than most other groups. Poverty and discrimination are key drivers in health disparities. Our research can be used to develop evidence-based policy that can ameliorate existing disparities by highlighting population groups that can benefit the most from interventions.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Sarah Buxbaum - Other, Florida A&M University

AD genetics (v7)

We plan to use the All of US data to conduct GWAS and Polygenic Risk score Analyses(PRS) in Alzheimer’s disease (AD). Standard AD PRSs are derived from (i) common SNPs only, (ii) make no use of AD-relevant multi-omic data, and…

Scientific Questions Being Studied

We plan to use the All of US data to conduct GWAS and Polygenic Risk score Analyses(PRS) in Alzheimer’s disease (AD). Standard AD PRSs are derived from (i) common SNPs only, (ii) make no use of AD-relevant multi-omic data, and (iii) distil the effects of SNPs across the genome to a single number, resulting in a key loss of information about an individual’s genetic profile. In contrast, we will compute pathway PRSs that aggregate rare, structural and common AD risk variants.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Population Health
Methods Development
Control Set
Ancestry

Scientific Approaches

We will be using the genetic data, and phenotype data from the Survey information including self-report and family health history about dementia. Related disease phenotype and clinical risk factors such as memory loss, CVD, diabetes and lifestyle will also be investigated in the analyses. We will use PLINK2 to perform a whole genome association analysis including the common and rare varaints on dementia/ AD case-control status. PRSice will be used to calculate genome-wide PRS and set-based PRS. Further regression and survival analyses will be ran in R.

Anticipated Findings

Our AD-tailored, pathway-based PRSs may enable stratification of AD patients into more homogeneous sub-types and uncover novel drug targets for treating AD patients of the corresponding sub-types.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

HEI MAN WU - Research Fellow, Icahn School of Medicine at Mount Sinai

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