Kelsey Mayo
All of Us Program Operational Use
4 active projects
Duplicate of Demo - Siloed Analysis of All of Us and UK Biobank Genomic Data
Scientific Questions Being Studied
Historically, researchers responded to limitations in genomic data sharing policy and practice by conducting meta analysis on summary outputs from isolated genomic datasets. Recent work has demonstrated the increased power of individual-level genetic analysis on pooled datasets. In addition, advancements in data access and sharing policies coupled with technological advancements in cloud-based environments for data access and analysis have opened up new possibilities for pooled analysis of large-scale genomic datasets. The NIH All of Us Research Program and UK Biobank are two leading examples of large, population scale studies which combine genomic data with deep phenotypic health data. There is a grand opportunity to demonstrate how the world’s largest research-ready biomedical datasets can create more value together and advance discovery in genome science.
Project Purpose(s)
- Other Purpose (This is a demonstration project meant to support research with All of Us genomic data. Please see https://www.biorxiv.org/content/10.1101/2022.11.29.518423)
Scientific Approaches
The primary goal of this project is to demonstrate the potential of the All of Us Researcher Workbench for pooled analyses of All of Us and UK Biobank data. Specifically, we aim to: 1. Develop and describe an approved, secure path for connecting UK Biobank data to the All of Us Researcher Workbench. 2. Conduct a genome-wide association study of blood lipids on the pooled dataset aimed at demonstrating that biomedical researchers can be more productive when permitted to analyze the union of the cohorts, as opposed to computing aggregate results in separate data silos for each cohort and then combining those aggregates.
Anticipated Findings
The secondary goal of this project is to demonstrate and measure the experience when the same analyses are repeated in a siloed manner. Specifically we aim to: 3. Repeat the previously described genome-wide association study on the All of Us Researcher Workbench when working with the All of Us data and on UK Biobank’s DNAnexus when working with the UK Biobank data. 4. Conduct a meta analysis on the aggregate results for each cohort (in accordance with each program’s data use policies) and compare the result of combining those aggregates to the results from the pooled analysis. Evaluate not only differences in results, but also differences in analysis cost and analyst productivity.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Kelsey Mayo - Other, All of Us Program Operational Use
- Jessica Hamblin - Graduate Trainee, Pennsylvania State University
- Nicole Deflaux - Other, All of Us Program Operational Use
- CH Albach - Other, All of Us Program Operational Use
Collaborators:
- Margaret Sunitha Selvaraj - Research Fellow, Broad Institute
- Melissa Patrick - Project Personnel, All of Us Program Operational Use
- Jennifer Zhang - Project Personnel, All of Us Program Operational Use
- Gage Rion - Project Personnel, All of Us Program Operational Use
- David Glazer - Other, All of Us Program Operational Use
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
- Alexander Bick - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
Duplicate of Demo - Siloed Analysis of All of Us and UK Biobank Genomic Data
Scientific Questions Being Studied
Historically, researchers responded to limitations in genomic data sharing policy and practice by conducting meta analysis on summary outputs from isolated genomic datasets. Recent work has demonstrated the increased power of individual-level genetic analysis on pooled datasets. In addition, advancements in data access and sharing policies coupled with technological advancements in cloud-based environments for data access and analysis have opened up new possibilities for pooled analysis of large-scale genomic datasets. The NIH All of Us Research Program and UK Biobank are two leading examples of large, population scale studies which combine genomic data with deep phenotypic health data. There is a grand opportunity to demonstrate how the world’s largest research-ready biomedical datasets can create more value together and advance discovery in genome science.
Project Purpose(s)
- Educational
- Other Purpose (This is a demonstration project meant to support research with All of Us genomic data. Please see https://www.biorxiv.org/content/10.1101/2022.11.29.518423)
Scientific Approaches
The primary goal of this project is to demonstrate the potential of the All of Us Researcher Workbench for pooled analyses of All of Us and UK Biobank data. Specifically, we aim to: 1. Develop and describe an approved, secure path for connecting UK Biobank data to the All of Us Researcher Workbench. 2. Conduct a genome-wide association study of blood lipids on the pooled dataset aimed at demonstrating that biomedical researchers can be more productive when permitted to analyze the union of the cohorts, as opposed to computing aggregate results in separate data silos for each cohort and then combining those aggregates.
Anticipated Findings
The secondary goal of this project is to demonstrate and measure the experience when the same analyses are repeated in a siloed manner. Specifically we aim to: 3. Repeat the previously described genome-wide association study on the All of Us Researcher Workbench when working with the All of Us data and on UK Biobank’s DNAnexus when working with the UK Biobank data. 4. Conduct a meta analysis on the aggregate results for each cohort (in accordance with each program’s data use policies) and compare the result of combining those aggregates to the results from the pooled analysis. Evaluate not only differences in results, but also differences in analysis cost and analyst productivity.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Roshan Paudel - Early Career Tenure-track Researcher, Morgan State University
- Kelsey Mayo - Other, All of Us Program Operational Use
- Nicole Deflaux - Other, All of Us Program Operational Use
- CH Albach - Other, All of Us Program Operational Use
Collaborators:
- Margaret Sunitha Selvaraj - Research Fellow, Broad Institute
- Melissa Patrick - Project Personnel, All of Us Program Operational Use
- Jennifer Zhang - Project Personnel, All of Us Program Operational Use
- Gage Rion - Project Personnel, All of Us Program Operational Use
- David Glazer - Other, All of Us Program Operational Use
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
- Alexander Bick - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
*Duplicate* of Mental health and deaf patients - registered tier
Scientific Questions Being Studied
We propose to study if there are any measurable differences between deaf, Deaf, or Hard of Hearing (d/DHH) patients and hearing patients with respect to mental health treatment. As primary goal, we are interested in how doctors treat the d/DHH patients with medication compared to hearing people. Evidence suggests that d/DHH patients have a greater health burden compared to hearing people. Of particular concern, d/DHH who have mental health disorders including bipolar, major depressive disorder, and PTSD (with or without diagnoses) face barriers to proper treatment such as:
- Language barriers
- Lack of interpreters who understands medical background / literacy
- Discrimination
- Lack of counselors / psychologists who has knowledge about Deaf culture and ASL
- Discomfort; some deaf people are uncomfortable with going to counseling with an interpreter because of privacy reasons
Project Purpose(s)
- Disease Focused Research (disease of mental health, social and behavioral health)
- Social / Behavioral
- Methods Development
Scientific Approaches
- Describe the prevalence of d/DHH patients within the All of Us cohort using electronic health record phenotype definitions
- Describe the prevalence of patients with bipolar, major depressive disorder, and PTSD (with or without diagnoses) using electronic health record phenotype definitions
- Describe the demographics of d/DHH patients with bipolar, major depressive disorder, or PTSD
- Describe the demographics of hearing patients with bipolar, major depressive disorder, or PTSD
- Compare medication treatment pathways for the three mental health disorders between d/DHH patients and hearing patients. The comparison will be primarily descriptive and based on evidence-based recommendations for treatment by professional societies.
Anticipated Findings
The primary hypothesis is that d/DHH patients receive less evidence-based mediation therapy for the three conditions of interest then hearing patients with a similar mental health status and background (i.e., accounting for age, sex at birth, sociodemographic variables, and disease co-morbidity).
Demographic Categories of Interest
- Disability Status
- Access to Care
Data Set Used
Registered TierResearch Team
Owner:
- Richard Boyce - Mid-career Tenured Researcher, University of Pittsburgh
- Michael Lyons - Project Personnel, All of Us Program Operational Use
- Kelsey Mayo - Other, All of Us Program Operational Use
Collaborators:
- Srilakshmi Chaparala - Other, University of Pittsburgh
- Mitra Mosslemi - Graduate Trainee, University of Pittsburgh
- Olga Kravchenko - Research Fellow, University of Pittsburgh
- Joanne Hasskamp - Project Personnel, University of Pittsburgh
- Mahmoud Aarabi - Early Career Tenure-track Researcher, University of Pittsburgh
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Ansuman Chattopadhyay - Project Personnel, University of Pittsburgh
Systemic Disease and Glaucoma (Cloned)
Scientific Questions Being Studied
We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.
Project Purpose(s)
- Disease Focused Research (Primary open angle glaucoma)
- Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )
Scientific Approaches
We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.
Anticipated Findings
We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Roxana Loperena Cortes - Other, All of Us Program Operational Use
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Paulina Paul - Project Personnel, University of California, San Diego
- Melissa Patrick - Project Personnel, All of Us Program Operational Use
- Lucila Ohno-Machado
- Luca Bonomi - Project Personnel, Vanderbilt University Medical Center
- Kelsey Mayo - Other, All of Us Program Operational Use
- Jihoon Kim - Project Personnel, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Ashley Able - Other, All of Us Program Operational Use
Collaborators:
- Chenjie Zeng - Research Fellow, National Human Genome Research Institute (NIH-NHGRI)
Request a Review of this Research Project
You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.
