Edwin Baldwin
Graduate Trainee, University of Arizona
6 active projects
COPC_v7_EB
Scientific Questions Being Studied
Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available.
Project Purpose(s)
- Disease Focused Research (Chronic overlapping pain conditions (COPC))
- Educational
Scientific Approaches
We will use logistic regression to study the pairwise overlapping, counting the time series of the occurrence of the diseases. We will also use similar models with lasso to identify most relevant pairs and their trajectories.
Anticipated Findings
We expect to identify true COPC developing pairs and clusters, providing insights for the development of COPC conditions, and the underlying conditions, such as mental status.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Gender Identity
- Sexual Orientation
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Controlled TierResearch Team
Owner:
- Edwin Baldwin - Graduate Trainee, University of Arizona
Collaborators:
- Jungwei Fan - Early Career Tenure-track Researcher, Mayo Clinic
- Haiquan Li - Early Career Tenure-track Researcher, University of Arizona
COPC_Cohorts_Controlled
Scientific Questions Being Studied
Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available.
Project Purpose(s)
- Disease Focused Research (Chronic overlapping pain conditions (COPC))
- Educational
Scientific Approaches
We will use logistic regression to study the pairwise overlapping, counting the time series of the occurrence of the diseases. We will also use similar models with lasso to identify most relevant pairs and their trajectories.
Anticipated Findings
We expect to identify true COPC developing pairs and clusters, providing insights for the development of COPC conditions, and the underlying conditions, such as mental status.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
- Gender Identity
- Sexual Orientation
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Controlled TierSchrepf COPC Reproduction
Scientific Questions Being Studied
Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available
Project Purpose(s)
- Disease Focused Research (Chronic Overlapping Pain Conditions (COPC) )
- Methods Development
Scientific Approaches
We will use logistic regression to study the pairwise overlapping, counting the time series of the occurrence of the diseases. We will also use similar models with lasso to identify most relevant pairs and their trajectories.
Anticipated Findings
We expect to identify true COPC developing pairs and clusters, providing insights for the development of COPC conditions, and the underlying conditions, such as mental status.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
Data Set Used
Registered TierDuplicate of How to Work with All of Us Survey Data (v6)
Scientific Questions Being Studied
We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.
What should you expect?
By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.
Project Purpose(s)
- Educational
- Methods Development
- Other Purpose (This is an All of Us Tutorial Workspace created by the Researcher Workbench Support team. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)
Scientific Approaches
By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.
Anticipated Findings
By reading and running the notebooks in this Tutorial Workspace, researchers will learn the following:
- how to query the survey data,
- how to summarize PPI modules, and questions.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierT6 Update of COPC_New
Scientific Questions Being Studied
Chronic pains are often overlapping with each other, forming COPC. The project will use all of us data to identify COPC developing trajectories and genetic mechanisms once the genetic data is available
Project Purpose(s)
- Disease Focused Research (Chronic overlapping pain conditions (COPC))
- Educational
Scientific Approaches
We will use logistic regression to study the pairwise overlapping, counting the time series of the occurrence of the diseases. We will also use similar models with lasso to identify most relevant pairs and their trajectories.
Anticipated Findings
We expect to identify true COPC developing pairs and clusters, providing insights for the development of COPC conditions, and the underlying conditions, such as mental status
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Sex at Birth
Data Set Used
Registered TierResearch Team
Owner:
- Jungwei Fan - Early Career Tenure-track Researcher, Mayo Clinic
- Haiquan Li - Early Career Tenure-track Researcher, University of Arizona
- Edwin Baldwin - Graduate Trainee, University of Arizona
Collaborators:
- Wenting luo - Graduate Trainee, University of Arizona
- Reid Loeffler - Undergraduate Student, University of Arizona
Disease_convergence_and_lifestyle
Scientific Questions Being Studied
Multiple genetic polymorphisms have been identified for complex diseases, but relationships, such as the biological underpinning of genetic interactions, are still elusive. Epigenomic studies have shown that genetic variants may have convergent effects, which increase the risk of developing complex diseases and comorbidities. We aim to prioritize the genetic variants with convergent effects and diseases of excess epigenomic similarity from the abundant biological resources, such as ENCODE and GTEx. We will then study the agreement between the convergent effects and interactions of genetic variants in AllofUsRP and the agreement between disease epigenomic similarity and disease comorbidities in AllofUsRP. Lifestyle and environment exposures are critical risk factors, and their effects will be modeled as well. The research will help us understand disease mechanisms and missing heritability and foster applications like drug repositioning.
Project Purpose(s)
- Population Health
- Methods Development
- Ancestry
Scientific Approaches
We have developed an information-theoretical based similarity for quantifying the similarity of genetic variants and disease pairs from GTEx data. We have also developed a multi-omics integration method to quantify the overall similarity of genetic variants in ENCODE. We will extend the latter method to quantify the epigenomic similarity for disease pairs. We aim to use AllofUsRP for validating the genetic interactions between genetic variants and comorbidities. Further, we will use, logistic regression, LASSO, and deep learning methods to model diseases from lifestyles and genetic interactions.
Anticipated Findings
We expect to find many unexpected biological links between the effects of distinct genetic variants, which may explain the increased risk of diseases and comorbidities. With machine learning models, we will build disease prediction models, particularly those impacted heavily by lifestyles, such as cancers. The research will generate candidates for novel drug targets and drug repositioning approaches.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Haiquan Li - Early Career Tenure-track Researcher, University of Arizona
- Edwin Baldwin - Graduate Trainee, University of Arizona
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