Research Projects Directory

COVID_AD

I intend to study how COVID-19 impacts Alzheimer's Disease risk. I hope to also implement machine learning techniques to explore potential racial disparities.

Scientific Questions Being Studied

I intend to study how COVID-19 impacts Alzheimer's Disease risk. I hope to also implement machine learning techniques to explore potential racial disparities.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

I will utilize Electronic Health Record data available and survey data. Research methods include basic association tests and machine learning techniques.

Anticipated Findings

My anticipated findings will potentially show how COVID might affect AD risk. In addition, I am curious about how COVID vaccines are recorded in the EHR and hope to find how well documented they are.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Carly Rose - Graduate Trainee, Case Western Reserve 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

Alzheimer and AQI Correlation

We want to explore whether there's a connection between Alzheimer's and air quality. This is an important question because there's still a lot that's unknown about Alzheimer's and if just living in a place with a better AQI could help,…

Scientific Questions Being Studied

We want to explore whether there's a connection between Alzheimer's and air quality. This is an important question because there's still a lot that's unknown about Alzheimer's and if just living in a place with a better AQI could help, that correlation would be significant for both pushes to improve air quality and attempts to address Alzheimer's.

Project Purpose(s)

Educational

Scientific Approaches

We will be looking at air quality datasets as well as Alzheimer's datasets and seeing whether there is any correlation between location (and attached AQI) and rate of Alzheimer's in that locale.

Anticipated Findings

We don't know yet whether there will be a correlation but we expect that either way, it would provide value for us as a learning experience (being college students who are learning about how to conduct research).

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Z Choy - Undergraduate Student, Arizona State University

Collaborators:

Tillie Fernau - Undergraduate Student, Arizona State University
Jacob Roth - Undergraduate Student, Arizona State University
Jason Mongru - Undergraduate Student, Arizona State University
Jackie Dalton - Undergraduate Student, Arizona State University

AD PheWas

Alzheimer’s disease (AD) is a highly heterogeneous disease which a variety of brain pathological changes, progress trajectories and many risk factors exist among AD patients. Previously, we identified 2 AD subtypes with multi-omcis data collected from publicly accessible studies. In…

Scientific Questions Being Studied

Alzheimer’s disease (AD) is a highly heterogeneous disease which a variety of brain pathological changes, progress trajectories and many risk factors exist among AD patients. Previously, we identified 2 AD subtypes with multi-omcis data collected from publicly accessible studies. In this project, we aim to perform a Phenome-wide Associaion Study (PheWas) to test the association between generic variants of the differentially expressed genes (DEGs) identified from previous study and a large scale of phenome.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Ancestry

Scientific Approaches

The PheWas will be implemented in a form of multivariate logistic regression, with phenome as the dependent variable, and genotypes as independent variable adjusting for confounding variables such as Age, Sex and Principle Components (PCs) for ancestry.

Anticipated Findings

The project aims to identify novel associations between AD genetic variants and other clinical traits, and we expect to validate some existing associations between the genetic variants and some traits as well. The results will help researchers better understand the connection between AD and other diseases, and we can utilize the results to better understand AD etiology, and contribute to AD diagnosis, treatment, and disease management.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ziyan Song - Graduate Trainee, Indiana University

Statins

Randomized controlled trials have consistently demonstrated the efficacy of statins, or 3-hydroxymethyl-3-methylglutaryl coenzyme A reductase inhibitors, in reducing cardiovascular morbidity and overall mortality, establishing them as standard treatments for primary and secondary prevention of cardiovascular disease. On the other hand,…

Scientific Questions Being Studied

Randomized controlled trials have consistently demonstrated the efficacy of statins, or 3-hydroxymethyl-3-methylglutaryl coenzyme A reductase inhibitors, in reducing cardiovascular morbidity and overall mortality, establishing them as standard treatments for primary and secondary prevention of cardiovascular disease. On the other hand, apolipoprotein E (APOE, https://www.ncbi.nlm.nih.gov/gene/348), a gene involved in lipid metabolism, is associated with increased risk of certain diseases, such as Alzheimer's disease among individuals with the APOE4 genotype. Recent reports (e.g. https://pubmed.ncbi.nlm.nih.gov/34306425/) suggest that polymorphisms in the APOE gene may influence the clinical response to statin therapy, prompting this study. Using data from the UK Biobank and the All of Us Research Program, our goal is to assess the impact of the APOE genotype on clinical outcomes of statin therapy, including changes in lipid levels.

Project Purpose(s)

Ancestry

Scientific Approaches

We are utilizing data from the UK Biobank, a large population-based prospective cohort study that includes comprehensive genetic and phenotypic data, along with linked primary health care records for approximately 230,000 participants. Using baseline data from the UK Biobank, we will examine the association between APOE genotype/carrier status and various lipid biomarkers, as well as clinical outcomes such as mortality. Additionally, by leveraging linked primary-care data from both the UK Biobank and the All of Us Research Program, we will analyse the association between APOE genotype/carrier status and the percentage change in biomarker levels following statin treatment, as well as the relationship between APOE genotype/carrier status and time to all-cause or ischemic heart disease-related death using Cox proportional hazards modelling.

Anticipated Findings

Understanding the impact of APOE genotype on the clinical outcomes of statin therapy is a critical step toward personalizing lipid-lowering treatments. It will help identify patients who are most likely to benefit from the therapy and those who may need closer monitoring or medication adjustments.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Innocent Gerald Asiimwe - Research Fellow, University of Liverpool

ADRD & SDoH

We aim to evaluate the impacts of area-level social determinants of health on incident Alzheimer's disease and other related dementias.

Scientific Questions Being Studied

We aim to evaluate the impacts of area-level social determinants of health on incident Alzheimer's disease and other related dementias.

Project Purpose(s)

Disease Focused Research (Dementia)
Social / Behavioral

Scientific Approaches

In All of Us, in keeping with previously validated work, we will use linkages of EHR data (updated quarterly) on those age 50+ to identify incident ADRD, based on one of the following: 1) a medical claim with ADRD diagnosis codes (the same above ICD-10 codes we will use for HRS) in any header position in an inpatient setting; or 2) a medical claim with a ADRD diagnosis code followed by another claim with a ADRD diagnosis code, with both claims in any setting, and the codes in any header position. To capture incident ADRD, we will require a 24-month period prior to the index date with no ADRD diagnosis or a pharmacy claim for donepezil hydrochloride, galantamine hydrobromide, rivastigmine tartrate, or tacrine hydrochloride, and then follow for incident dementia events over a 5-year period from July 1, 2020 to June 30, 2025.

Anticipated Findings

We anticipate to identify ADRD incidence and identify protective factors against incident ADRD that may be future targets for intervention or policy.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Elizabeth Davis - Graduate Trainee, Northeastern University

kristin_med_explore

We are looking into associations between medications and various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, MS, vascular dementia, etc.

Scientific Questions Being Studied

We are looking into associations between medications and various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, MS, vascular dementia, etc.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease and other NDDs)
Population Health

Scientific Approaches

We will use EHR data and medication data to examine these associations. We will run a Cox regression and right censor the data to only include medication exposures that occur BEFORE an NDD diagnosis.

Anticipated Findings

We are looking to replicate similar findings from UKB and provide more information about the benefits/risks to the use of certain drugs.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Kristin Levine - Project Personnel, National Institute on Aging (NIH - NIA)

Collaborators:

Vanessa Pitz - Research Fellow, National Institute on Aging (NIH - NIA)
Emma Somerville - Graduate Trainee, National Institute on Aging (NIH - NIA)

Alzheimer's-Environmental exposure (mycotoxin)

How do environmental factors contribute to the development of Alzheimer’s disease? If exposure to environmental pollutants is reduced or eliminated, does this affect the development of this disease? (More specifically, pesticides, toxic chemical exposure-mycotoxin)

Scientific Questions Being Studied

How do environmental factors contribute to the development of Alzheimer’s disease? If exposure to environmental pollutants is reduced or eliminated, does this affect the development of this disease? (More specifically, pesticides, toxic chemical exposure-mycotoxin)

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Educational

Scientific Approaches

Using the All of Us data set and workbench along with other peer reviewed articles to provide evidence or correlations surrounding environmental exposure to mycotoxin and Alzheimer's.

Anticipated Findings

Following data analysis of findings which will either determine a correlation to these specific environmental factors or not. This will be presented using charts and graphs that effectively communicate the results.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Erin Delaney - Undergraduate Student, Arizona State University
allison sabine - Undergraduate Student, Arizona State University

Alzheimer's-Mycotoxin (Environmental exposure)

How do environmental factors contribute to the development of Alzheimer’s disease? If exposure to environmental pollutants is reduced or eliminated, does this affect the development of this disease? (More specifically, pesticides, toxic chemical exposure-mycotoxins)

Scientific Questions Being Studied

How do environmental factors contribute to the development of Alzheimer’s disease? If exposure to environmental pollutants is reduced or eliminated, does this affect the development of this disease? (More specifically, pesticides, toxic chemical exposure-mycotoxins)

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Educational

Scientific Approaches

All of Us workbench along with peer reviewed studies to support demographic analysis of the question. Once analyzed a brief explanation of findings and charts will be produced to effectively communicate findings.

Anticipated Findings

If any correlations can be made with Alzheimer disease and different environmental toxins and exposures, more specifically mycotoxin exposure.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Erin Delaney - Undergraduate Student, Arizona State University

peri-menopausal stage with insomnia

Insomnia is associated with worsened clinical outcomes among Alzheimer’s disease dementia (AD) patients, increased caregiver burden, and healthcare utilization. So we are trying to identify patients at peri-menopausal stage, with insomnia complaints or not, tracking the cognitive trajectory.

Scientific Questions Being Studied

Insomnia is associated with worsened clinical outcomes among Alzheimer’s disease dementia (AD) patients,
increased caregiver burden, and healthcare utilization. So we are trying to identify patients at peri-menopausal stage, with insomnia complaints or not, tracking the cognitive trajectory.

Project Purpose(s)

Disease Focused Research (insomnia)
Methods Development

Scientific Approaches

cohort study, compare the peri-menopausal stage women who are diagnosed insomnia and who are not, based on tracking the cognitive trajectory (% developing cognitive complaints and time interval of developing cognitive complaints; disease progression from MCI to dementia (time interval).

Anticipated Findings

By getting a comparative results of two groups of women, providing insights of insomnia treatment, providing some proof-of-concept results to determine if any association between peri- and post-menopausal insomnia and cognitive decline/dementia incidence.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Han Yang - Graduate Trainee, University of Minnesota
Yu Hou - Early Career Tenure-track Researcher, University of Minnesota

Collaborators:

Zhiyu Kang - Graduate Trainee, University of Minnesota

Dementia-Alzheimer's-CRP

Do CRP levels change with age in dementia/Alzheimer's patients as opposed to controls? Associations have shown that these protein levels are increased in Alzheimer's patients, and this will help answer whether patients' CRP levels are elevated naturally or if it…

Scientific Questions Being Studied

Do CRP levels change with age in dementia/Alzheimer's patients as opposed to controls? Associations have shown that these protein levels are increased in Alzheimer's patients, and this will help answer whether patients' CRP levels are elevated naturally or if it is something that happens over time as a result of the disease progression.

Project Purpose(s)

Disease Focused Research (Dimentia/Alzheimer's)

Scientific Approaches

Datasets - AOU phenotyping and lab data Research methods and tools - clean and summarize lab data for CRP - average by decile of age and disease status and plot in R.

Anticipated Findings

I anticipate that CRP levels will be elevated in Alzheimer's after disease progression. This would help answer cause and effect of the two

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Slavina Goleva - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
David Schlueter - Research Fellow, National Human Genome Research Institute (NIH - NHGRI)

Collaborators:

Anirudh Kesanapally - Research Assistant, National Human Genome Research Institute (NIH - NHGRI)

SVs

Understanding genomic variability is pivotal for unraveling the molecular intricacies underlying human diseases, offering critical insights that drive precision medicine and innovative therapeutic interventions. This study, for the first time, investigates the role of both short and long, rare and…

Scientific Questions Being Studied

Understanding genomic variability is pivotal for unraveling the molecular intricacies underlying human diseases, offering critical insights that drive precision medicine and innovative therapeutic interventions. This study, for the first time, investigates the role of both short and long, rare and common germline genomic variants in modulating the risk of developing YOAD. By including the largely unexplored germline SVs, this research aims to fill a significant gap in our understanding of sporadic YOAD, providing the first comprehensive assessment of the genetic factors that may influence the AD phenotype. The results of this study could significantly advance our ability to diagnose and treat Alzheimer's disease, begin to shed light on the different forms, etiologies, and pathways involved in AD, and improve our understanding of the mechanisms involved in the development of dementia.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)
Control Set
Ancestry

Scientific Approaches

We will select a cohort of 21 YOAD patients and 100 healthy controls, matched by age and sex. To enhance the accuracy of common and rare polymorphism detection, we will supplement our dataset with external LRS data from healthy subjects obtained from the All of Us, T2T consortium, UK Biobank and more. Rare variants (MAF ≤ 0.1%) will be analyzed using the SKAT-O and burden tests, while common variants (MAF ≥ 1%) will be analyzed through GWAS using the BOLT-LMM3 algorithm. Our results will be cross-referenced with previous AD-specific GWAS to identify variants in proximity to known AD-related SNPs, quantitative trait loci (QTLs) as well as for other neuropsychiatric disorder risk loci. We will calculate the AD-related PRS. Finally, an additional cohort of sporadic AD patients, will be used to identify potential differences in genetic risk. Finally, we will use all the identify variants to create a new YOAD-PRS.

Anticipated Findings

The proposed study aims to definitely assess the role of genomic heterogeneity in the risk of develops sporadic YOAD. By leveraging LRS, we will be able to comprehensively assess the distribution of small and large variants, enabling three critical areas of focus: (1) in basic science, the potential to identify new variants, genes and pathway involved in AD phenotype anticipation; (2) the ability to study the biological and molecular changes occurring in YOAD patients helping to elucidate the general molecular mechanisms that lead to AD and dementia; and, (3) in clinical science, the potential to use these variants as clinical biomarkers to improve the diagnosis, and treatment of patients, as well as design more tailored and pre-symptomatic clinical trials. Additionally, by including underrepresented ancestries, we will be able to apply the main findings from the predominantly European ancestry group to other populations that are typically underrepresented in genomic research.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Fabrizio Ecca - Research Fellow, Translational Genomics Research Institute

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
JIng Zhang - Research Fellow, University of Kentucky
Inori Tsuchiya - Project Personnel, University of Kentucky

AD disparity

Alzheimer's Disease (AD) is debilitating conditions that impair memory, thought processes, and functioning of millions of primarily older adults, in the US and worldwide. AD arise from complex interactions between genetic (G) and environmental (E) factors with ~70% of risk…

Scientific Questions Being Studied

Alzheimer's Disease (AD) is debilitating conditions that impair memory, thought processes, and functioning of millions of primarily older adults, in the US and worldwide. AD arise from complex interactions between genetic (G) and environmental (E) factors with ~70% of risk of developing AD attributable to genetics and acquired environmental, social and lifestyle risk factors throughout lifespan also contribute significantly to the AD development and progression. Racial disparities exist in AD that older adults from racially minority populations were more likely than white populations to have poorer cognitive functioning, accelerated cognitive decline, and higher AD prevalence. However, the risk factors that contribute to this racial gap and the underlying mechanisms that produce the AD inequality remain largely unknown. In this project, we aim to identify the genetic, social, lifestyle and other environmental risk factors that lead to the racial disparity in AD.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease and psychiatric disorders (e.g. addiction))
Population Health
Methods Development
Ancestry

Scientific Approaches

In this study, we will analyze different types of data (e.g. genetic, multi-omics, survey data, etc.) from a number of racially diverse cohorts including All of Us. For All of Us, we will analyze the genetic data from both white and minority populations and identify the genetic risk factors that drive the racial disparity in AD. We will use trans-ancestry genome-wide association study (GWAS) methods followed by transcriptome-wide association study (TWAS), polygenic risk score analysis and fine mapping tools. We will also analyze the survey questionnaire, electronic health record (EHR) and physical measurement data and apply machine learning methods (e.g. transfer learning methods, neural network based discriminative models such as CNN, GCN, etc.) to identify the social (e.g. SDOH), lifestyle (e.g. LE8) and other environmental risk factors that drive the racial disparity in AD.

Anticipated Findings

In this study, we aim to identify the genetic, social, lifestyle and other environmental risk factors that lead to the racial disparity in AD, with the goal of improving the mechanistic understanding of the aging brain underlying the pathogenesis of AD. In addition, we will also evaluate how health resource utilization, institutionalization and treatment effectiveness of AD differ in different racial groups. The findings will provide us a holistic picture of racial disparity in AD and suggest prevention strategy to lessen the disparity. Our study will also potentially provide possible solutions to clinical implications for reducing AD burden and racial/ethnic AD disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Tianzhou Ma - Mid-career Tenured Researcher, University of Maryland, College Park

IADRC

Individuals with Alzheimer’s disease and related dementia (ADRD) often experience a significant burden of comorbid conditions. Understanding the interconnections between these comorbidities and ADRD is crucial for advancing knowledge of their etiology and pathogenesis. Recent network analyses utilizing large electronic…

Scientific Questions Being Studied

Individuals with Alzheimer’s disease and related dementia (ADRD) often experience a significant burden of comorbid conditions. Understanding the interconnections between these comorbidities and ADRD is crucial for advancing knowledge of their etiology and pathogenesis. Recent network analyses utilizing large electronic medical records (EMR) and related biobank data have focused on pairwise cross-sectional associations, overlooking the sequential nature of comorbid disease onset. We propose a life course approach to analyze longitudinal EMR data enriched with genome-wide association study (GWAS) data. We will utilize advanced multivariate time-to-event models to characterize disease onset trajectories in EMR data while accounting for competing mortality risks. Our objectives are to identify dynamic networks of comorbid diseases associated with ADRD and to discover novel genetic variants linked to both comorbid diseases and ADRD.

Project Purpose(s)

Disease Focused Research (Aging)
Population Health
Methods Development
Ancestry

Scientific Approaches

We will use a life course approach to analyze longitudinal EMR data enriched with genome-wide association study (GWAS) data. We will utilize advanced multivariate time-to-event models to characterize disease onset trajectories in EMR data while accounting for competing mortality risks. Our objectives are to identify dynamic networks of comorbid diseases associated with ADRD and to discover novel genetic variants linked to both comorbid diseases and ADRD. We will use EMR and GWAS data from All of Us with analytical programs developed using R.

Anticipated Findings

We anticipate the following results:
1. Identify disease networks associated with Alzheimer's disease.
2. Identify genetic variants associated with disease networks.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sujuan Gao - Late Career Tenured Researcher, Indiana University

Collaborators:

Minmin Pan - Graduate Trainee, Indiana University
Dongbing Lai - Project Personnel, Indiana University
Steven Brown - Project Personnel, Indiana 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

Neurological disease and metabolic dysfunction

1. In the data, which genetic variants are implicated in the aforementioned diseases? a. Are there any rare variants that are implicated in Parkinson’s, Alzheimer’s, etc. in the data? 2. Can we infer the mode of inheritance for disease correlated…

Scientific Questions Being Studied

1. In the data, which genetic variants are implicated in the aforementioned diseases?
a. Are there any rare variants that are implicated in Parkinson’s, Alzheimer’s, etc. in the data?
2. Can we infer the mode of inheritance for disease correlated variants?
3. If disease-linked rare variants exist in the data, does the available phenotype, lineage, environmental, and age information align with disease-linked rare variants found in the literature (but not in the All of Us database).
4. How (diseased or not) many happened to be taking terazosin, doxazosin, or alfuzosin? How did diseased phenotypes compare between those who were prescribed these medications versus not.

Project Purpose(s)

Disease Focused Research (Parkinson's, Alzheimer's, Lewy-Body Dementia, ALS, and Huntington's)
Population Health
Educational
Ancestry

Scientific Approaches

1. We will begin with a general approach, generating two cohorts – those with confirmed disease and those without. From here we will subset the disease group into cohorts with a specific disease. We can continue to subset the cohorts to perform several comparisons, controlling for several factors like phenotype, genotype, demographics (age, sex, occupation), medication, geography, etc.
a. First comparisons will be done using Venn diagrams.
b. More advanced comparisons will be made using a general linear model to determine which factors disproportionally correlate with disease phenotype.

Anticipated Findings

1. We expect to find genetic variants that have historically been overlooked as causers of the diseases mentioned above.
a. We also expect to find patterns among shared geography, age range, sex, occupation
2. For diseased individuals with a history of taking terazosin, doxazosin, or alfuzosin, we anticipate a difference in reported severity of disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Mitchell Riley - Research Fellow, University of Iowa

Approaches to Understanding Medical Conditions Using Machine Learning

We are exploring the data at this stage to find patterns and connections in those who have Alzheimer's disease in the United States. This will involve machine learning and statistical methods to analyze the behavior and prevalence of Alzheimer's disease.…

Scientific Questions Being Studied

We are exploring the data at this stage to find patterns and connections in those who have Alzheimer's disease in the United States. This will involve machine learning and statistical methods to analyze the behavior and prevalence of Alzheimer's disease. Although the research question is not exactly specific at this stage, using the data we hope to answer questions about commonalities in Alzheimer's patients and how that relates to recognizing it early on. This research question has room to be updated and made more specific as more time goes on working on this project.

Project Purpose(s)

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

Scientific Approaches

After cleaning and pre-processing the data, we aim to construct predictive models using machine learning algorithms and statistical methods. Additionally, we aim to create data visualization models to better understand the behavior and prevalence of Alzheimer's in the United States. The data that will be looked at will span genders, age, location, etc, ensuring a diverse dataset of all types of people. Specific tools have not been determined at this time, as the first period will focus on data collection.

Anticipated Findings

For this study, we anticipate finding commonalities between those with Alzheimer's disease across the country. Finding specific trends in people who have Alzheimer'd disease can provide insight on how to recognize it early and predict how it behaves. Our findings would further discussion within the community, and aid in providing more statistical data that can be studied. These findings could be beneficial to many.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Oliver Aschenbrenner - Undergraduate Student, College of Charleston
Navid Hashemi - Early Career Tenure-track Researcher, College of Charleston

Mental Health & Alzheimer's Disease

I will be looking at patients with diagnosed mental health conditions and what those conditions are. This will help me get an idea of how much data allofus has on mental health. I then will look into patients with Alzheimer's…

Scientific Questions Being Studied

I will be looking at patients with diagnosed mental health conditions and what those conditions are. This will help me get an idea of how much data allofus has on mental health. I then will look into patients with Alzheimer's disease and see how much data allofus has on this disease. Once I get a good understanding of the patients with mental health conditions and/or Alzheimer disease I will look at patients with Alzheimer's disease that also have a mental health condition. This will help identify if there is a possible correlation between having a mental health condition and developing Alzheimer's disease. This is relevant to science because if these conditions are found to be related then this could help the science community create a better treatment method.

Project Purpose(s)

Educational

Scientific Approaches

I will be looking at patients with diagnosed mental health conditions and what those conditions are. This will help me get an idea of how much data allofus has on mental health. I then will look into patients with Alzheimer's disease and see how much data allofus has on this disease. Once I get a good understanding of the patients with mental health conditions and/or Alzheimer disease I will look at patients with Alzheimer's disease that also have a mental health condition. This will help identify if there is a possible correlation between having a mental health condition and developing Alzheimer's disease.

Anticipated Findings

Based on research articles I have been reading there seems to be a link in mental health conditions and developing Alzheimer's. I am hoping to see this correlation within the allofus data. This research will contribute to the body of science research by verifying the link as claimed in literature and by looking into the similarities between patients will allow for further research to be performed.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Christina Newman - Undergraduate Student, Arizona State University

Genetic Analysis

1. Understanding Genetic Risk Factors: Investigating the association between the APOE gene and ADRD is crucial for understanding the genetic risk factors contributing to the development of Alzheimer's disease and related dementias. APOE is one of the most widely studied…

Scientific Questions Being Studied

1. Understanding Genetic Risk Factors: Investigating the association between the APOE gene and ADRD is crucial for understanding the genetic risk factors contributing to the development of Alzheimer's disease and related dementias. APOE is one of the most widely studied genetic risk factors for late-onset Alzheimer's disease.
2. Clinical Implications: Identifying genetic markers associated with ADRD can have significant clinical implications. It can aid in early diagnosis, risk assessment, and personalized treatment approaches. Understanding the strength of the association can help in determining the relative risk conferred by specific APOE alleles.

Project Purpose(s)

Ancestry

Scientific Approaches

1. Data Collection and Datasets:Access population-based cohort studies with longitudinal data to assess the relationship between APOE genotype and the development of ADRD over time. 2. Genetic Analysis: Conduct genetic association analyses to assess the association between APOE genotype and ADRD risk. 3. Statistical Modeling.

Anticipated Findings

1. Significant Association: The study may reveal a statistically significant association between specific variants of the APOE gene (e.g., ε4 allele) and an increased risk of developing ADRD. This finding would corroborate previous research highlighting APOE as a major genetic risk factor for late-onset Alzheimer's disease and potentially for other related dementias as well.
2. Allelic Effects: There may be differences in the strength of the association between different APOE alleles (e.g., ε2, ε3, ε4) and ADRD risk. For instance, individuals carrying the ε4 allele may exhibit a higher risk compared to those with ε3 or ε2 alleles, with ε2 potentially being protective against ADRD.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yu Hou - Early Career Tenure-track Researcher, University of Minnesota

Collaborators:

Jinhua Wang - Mid-career Tenured Researcher, University of Minnesota
Sayeed Ikramuddin - Late Career Tenured Researcher, University of Minnesota

Alzheimer's and Allergies

I am in the process of formalizing a research question for undergraduate research. I am going to study the relationship between Alzheimer's disease and allergies/allergic disease.

Scientific Questions Being Studied

I am in the process of formalizing a research question for undergraduate research. I am going to study the relationship between Alzheimer's disease and allergies/allergic disease.

Project Purpose(s)

Disease Focused Research (Alzheimer's disease)

Scientific Approaches

I will use patient records and the cohort tool. I will add to this description when I am more familiar with the database.

Anticipated Findings

I expect to find a correlation between a history of allergies and allergic disease, and Alzheimer's disease. Whether I do find a correlation or not, this will add to our understanding of potential Alzheimer's risk factors.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kelsey Adkinson - Undergraduate Student, Arizona State University

Genetic, environmental, and social drivers of health in dementia

Neurodegenerative disorders such as Alzheimer’s disease and related dementias are multifactorial diseases influenced by genetic, environmental and social drivers of health. Research on AD and related dementias has predominantly focused on genetic and environmental risk factors within the non-Latinx White…

Scientific Questions Being Studied

Neurodegenerative disorders such as Alzheimer’s disease and related dementias are multifactorial diseases influenced by genetic, environmental and social drivers of health. Research on AD and related dementias has predominantly focused on genetic and environmental risk factors within the non-Latinx White population. This lack of diversly available genetic and phenotypic datasets has made it difficult to understand how these factors interact across diverse populations. Consequently, the effects of genetic, environmental and social drivers of health in diverse populations remain poorly understood. To fully understand the pathogenesis of neurodegenerative disorders, we need to examine the interaction between these factors across diverse populations, leading to improved diagnostics and therapeutics. Using All of Us data we aim to determine the independent and joint effects of genetic, environmental, and social drivers of health on neurodegenerative disorders in diverse populations.

Project Purpose(s)

Disease Focused Research (dementia)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

EHR and survey data will be used to extract information on family history of neurodegenerative disorders, social determinants of health, and clinical, lifestyle, and behavioral risk factors. Genetic data will be used to estimate genetic ancestry, conduct genome-wide association studies, and generate polygenic risk scores (PRS).
We will conduct the following analysis:
- Regression and machine learning models will be used to evaluate the association of genetic, environmental, and social drivers of health with neurodegenerative disorders.
- Genome-wide association studies of selected traits will be conducted to generate summary statistics to conduct polygenic risk scores, genetic correlations, and Mendelian randomization analyses to identify causal risk factors for neurodegenerative disorders

Anticipated Findings

We expect that genetic, environmental, and social drivers to be associated with neurodegenerative disorders. Additionally, we anticipate environmental and social factors will moderate the association of genetic liability for dementia with neurodegenerative disorders. Furthermore, differences in risk profiles across diverse populations will enhance the development of precision medicine. Tailoring prevention and treatment strategies to individual risk profiles can significantly improve health outcomes for diverse populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Shea Andrews - Early Career Tenure-track Researcher, University of California, San Francisco

Collaborators:

Rahul Gupta - Graduate Trainee, Broad Institute
Ana Boeriu - Early Career Tenure-track Researcher, University of California, San Francisco

Duplicate of LOXY

We aim to identify the genetic determinants of Loss of the Y chromosome (LOY) in diverse populations and examine how LOY correlates with age-related diseases such as cardiovascular diseases, Alzheimer's, and cancer. Additionally, we will explore environmental and lifestyle factors…

Scientific Questions Being Studied

We aim to identify the genetic determinants of Loss of the Y chromosome (LOY) in diverse populations and examine how LOY correlates with age-related diseases such as cardiovascular diseases, Alzheimer's, and cancer. Additionally, we will explore environmental and lifestyle factors influencing LOY. This research could reveal new genetic insights, enhance disease prediction, and inform targeted interventions.

We plan to analyze All Of Us data to identify patterns and correlations related to LOY. This exploration will help us generate testable hypotheses for further study, leveraging the dataset’s diverse participant base and comprehensive health information.

We aim to advance understanding of LOY, leading to improved public health strategies. Identifying LOY as a biomarker for age-related diseases could aid in early diagnosis and better disease management, reducing morbidity and mortality in aging populations. Insights from this study could also inform personalized medicine approaches.

Project Purpose(s)

Disease Focused Research (Loss of Chromosome Y)
Population Health
Social / Behavioral
Educational
Drug Development
Methods Development
Control Set
Ancestry
Other Purpose (Testing methods and validating pipeline for main project.)

Scientific Approaches

We will use genotype and phenotype data from the All Of Us research program, which offers comprehensive genetic information and diverse health records.

We will use our BaySeq-Y method to analyze the whole genome and exome sequencing data to estimate the mosaicism of LOY. We will identify common and rare variants associated with LOY. A bootstrapping-based framework will assess the effects of rare variants on LOY risk. We will also analyze lifestyle and environmental factors influencing LOY. Finally, we will calculate and integrate polygenic risk scores with rare variant risk scores to derive a comprehensive genetic risk score, enhancing LOY prediction and its associated age-related diseases. By using advanced genomic, statistical, and bioinformatic tools, we aim to uncover genetic and environmental determinants of LOY, deepening our understanding of its role in aging and age-related diseases, paving the way for novel interventions and personalized medicine strategies.

Anticipated Findings

We expect to identify specific genetic variants, both common and rare, that contribute to Loss of the Y chromosome (LOY) and its association with age-related diseases. Additionally, we aim to uncover environmental and lifestyle factors influencing LOY. These findings will enhance our understanding of LOY as a biomarker for age-related health risks and guide future research on prevention and intervention strategies. By clarifying the genetic and environmental interplay, our study will contribute to the field of aging research, potentially leading to targeted therapies that improve the quality of life for aging populations. Overall, our work will advance personalized medicine approaches related to aging and genomic instability.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Zhengdong Zhang - Mid-career Tenured Researcher, Albert Einstein College of Medicine
Fitzgerald Small - Graduate Trainee, Albert Einstein College of Medicine

Collaborators:

Jhih-Rong Lin - Senior Researcher, Albert Einstein College of Medicine

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
Ole Andreassen - Late Career Tenured Researcher, University of Oslo
Iris Broce - Early Career Tenure-track Researcher, University of California, San Diego
Gisele Sanda - Project Personnel, University of California, San Diego
Elise Koch - Research Fellow, University of Oslo

Collaborators:

Sara Stinson - Research Fellow, Oslo University Hospital
Alexey Shadrin - Research Fellow, University of Oslo

LOXY

We aim to identify the genetic determinants of Loss of the Y chromosome (LOY) in diverse populations and examine how LOY correlates with age-related diseases such as cardiovascular diseases, Alzheimer's, and cancer. Additionally, we will explore environmental and lifestyle factors…

Scientific Questions Being Studied

We aim to identify the genetic determinants of Loss of the Y chromosome (LOY) in diverse populations and examine how LOY correlates with age-related diseases such as cardiovascular diseases, Alzheimer's, and cancer. Additionally, we will explore environmental and lifestyle factors influencing LOY. This research could reveal new genetic insights, enhance disease prediction, and inform targeted interventions.

We plan to analyze All Of Us data to identify patterns and correlations related to LOY. This exploration will help us generate testable hypotheses for further study, leveraging the dataset’s diverse participant base and comprehensive health information.

We aim to advance understanding of LOY, leading to improved public health strategies. Identifying LOY as a biomarker for age-related diseases could aid in early diagnosis and better disease management, reducing morbidity and mortality in aging populations. Insights from this study could also inform personalized medicine approaches.

Project Purpose(s)

Disease Focused Research (Loss of Chromosome Y)
Population Health
Social / Behavioral
Drug Development
Methods Development
Control Set
Ancestry

Scientific Approaches

We will use genotype and phenotype data from the All Of Us research program, which offers comprehensive genetic information and diverse health records.

We will use our BaySeq-Y method to analyze the whole genome and exome sequencing data to estimate the mosaicism of LOY. We will identify common and rare variants associated with LOY. A bootstrapping-based framework will assess the effects of rare variants on LOY risk. We will also analyze lifestyle and environmental factors influencing LOY. Finally, we will calculate and integrate polygenic risk scores with rare variant risk scores to derive a comprehensive genetic risk score, enhancing LOY prediction and its associated age-related diseases. By using advanced genomic, statistical, and bioinformatic tools, we aim to uncover genetic and environmental determinants of LOY, deepening our understanding of its role in aging and age-related diseases, paving the way for novel interventions and personalized medicine strategies.

Anticipated Findings

We expect to identify specific genetic variants, both common and rare, that contribute to Loss of the Y chromosome (LOY) and its association with age-related diseases. Additionally, we aim to uncover environmental and lifestyle factors influencing LOY. These findings will enhance our understanding of LOY as a biomarker for age-related health risks and guide future research on prevention and intervention strategies. By clarifying the genetic and environmental interplay, our study will contribute to the field of aging research, potentially leading to targeted therapies that improve the quality of life for aging populations. Overall, our work will advance personalized medicine approaches related to aging and genomic instability.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Zhengdong Zhang - Mid-career Tenured Researcher, Albert Einstein College of Medicine

Collaborators:

Jhih-Rong Lin - Senior Researcher, Albert Einstein College of Medicine
Fitzgerald Small - Graduate Trainee, Albert Einstein College of Medicine

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