Research Projects Directory

SDoH factors and Racial/Ethnicity identity associated with Type 2 Diabetes

We will use the Type 2 Diabetes Mellitus phenotype as an example for studying SDOH variables using the Controlled Tier Curated Data Repository (CDR). We will investigate if SDOH factors or Racial and Ethnicity identity are more significantly associated with…

Scientific Questions Being Studied

We will use the Type 2 Diabetes Mellitus phenotype as an example for studying SDOH variables using the Controlled Tier Curated Data Repository (CDR). We will investigate if SDOH factors or Racial and Ethnicity identity are more significantly associated with Type 2.

Project Purpose(s)

Population Health

Scientific Approaches

We will start with a cohort of Type 2 Diabetes Mellitus with SDOH and self-identifying racial and ethnicity information available. We will characterize the SDOH factors and compare them to Type 2 Diabetes. We will quantify the associations through statistical methods while controlling for demographics and other factors.

Anticipated Findings

We hope these findings will help answer questions about health disparities. Historic trends strongly correlate racial/ethnic minorities and lower quality of SDoH. This study intends to look into how AllofUs is capturing these trends and whether a lack of overlap is due to other factors or missing data points.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jonathan Kim - Graduate Trainee, Arizona State University
Bishnu Sarker - Early Career Tenure-track Researcher, Meharry Medical College

Duplicate2of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kosaku Aoyagi - Early Career Tenure-track Researcher, University of Texas at El Paso

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

David Blair - Research Fellow, University of California, San Francisco

Impact of social and environmental exposures on the severity of Type 2 Diabetes

How do social and environmental exposures affect the severity of Type 2 diabetes (T2D) in the All of Us Cohort ? We will explore how social burdens (food and housing insecurity, social isolation, discrimination, perception of security) and environmental exposures…

Scientific Questions Being Studied

How do social and environmental exposures affect the severity of Type 2 diabetes (T2D) in the All of Us Cohort ?
We will explore how social burdens (food and housing insecurity, social isolation, discrimination, perception of security) and environmental exposures (built environments) influence the progression of T2D with genetic markers (Polygenic Risk Scores) associated with increased risk for T2D. By fostering a deeper understanding of chronic health's social and environmental drivers, we can empower communities to advocate for policies and initiatives that promote health equity and well-being that disproportionately affect the communities Federally Qualified Health Centers (FQHCs) serve.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Population Health
Ancestry

Scientific Approaches

Population of Interest: Participants with 3+ years of EHR history at same location and documented T2D in EHR or participants who have self-identified as having T2D on survey.
Outcome/Response Variable is the severity of T2D, based on A1C values over time (above/below a threshold). Predictors include social and environmental exposures (SDoH survey responses to specific questions of interest to the study, Area Deprivation Index from Neighborhood Atlas), treatment data (medication sequencing, density of EHR information) and other data as needed (Census, air quality, built environment, C-Reactive Protein, length of disease). Confounding Factors / Control Variables include Polygenic Risk Score (PRS) for diabetes, age. weight, number of medications.

Anticipated Findings

Identification of social and environmental risk factors associated with specific chronic disease.
Early intervention strategies based on knowledge of disease associated with specific social stressors and environmental factors, which may lead to early genetic testing, policy changes and educational programs.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Soumya Kini - Project Personnel, The MITRE Corporation
Justin Hill - Project Personnel, The MITRE Corporation

Collaborators:

May Oo - Research Associate, The Community Health Center
Christian Rodriguez - Project Personnel, San Ysidro Health

Heterogeneous treatment effects of diabetes medications

Does the effect of diabetes medications differ across individuals, and what determines the effect heterogeneity?

Scientific Questions Being Studied

Does the effect of diabetes medications differ across individuals, and what determines the effect heterogeneity?

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Drug Development
Methods Development
Ancestry

Scientific Approaches

We will construct causal machine learning models to estimate the individual-level effects of diabetes medications. Then, we will conduct a genome-wide association study of treatment response.

Anticipated Findings

This analysis of heterogeneous treatment effects of diabetes medications will contribute to precision medicine – we will be better able to predict how an individual might respond to drugs based on their individual-level characteristics.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ryunosuke Goto - Research Associate, Kyoto University

Collaborators:

Yuichiro Mori - Graduate Trainee, Kyoto University
Kosuke Inoue - Early Career Tenure-track Researcher, Kyoto University

GWAS of treatment effects of diabetes medications

Does the effect of diabetes medications differ across individuals, and what genetic variants are associated with the effect heterogeneity?

Scientific Questions Being Studied

Does the effect of diabetes medications differ across individuals, and what genetic variants are associated with the effect heterogeneity?

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Drug Development
Methods Development
Ancestry

Scientific Approaches

We will construct causal machine learning models to estimate the individual-level effects of diabetes medications. Then, we will use a genome-wide approach to identify genetic variants associated with the effects.

Anticipated Findings

This analysis of heterogeneous treatment effects of diabetes medications will contribute to precision medicine – we will be better able to predict how an individual might respond to drugs based on their individual-level characteristics.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ryunosuke Goto - Research Associate, Kyoto University

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jinyoung Byun - Other, Baylor College of Medicine

V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Julie Coleman - Project Personnel, Baylor College of Medicine

Collaborators:

Shamika Ketkar - Other, Baylor College of Medicine
Jasmine Baker - Research Fellow, Baylor College of Medicine
Elizabeth Atkinson - Early Career Tenure-track Researcher, Baylor College of Medicine
Zinhle Cindi - Research Fellow, University of Pennsylvania
Zijian Zhang - Research Fellow, Baylor College of Medicine
Yoruba Mutakabbir - Mid-career Tenured Researcher, Hampton University
Yan Ding - Research Fellow, Baylor College of Medicine
Wilson Poon - Early Career Tenure-track Researcher, University of Texas at El Paso
Ulanda Simpson - Other, Prairie View A&M University
Tulika Singh - Research Fellow, University of California, Berkeley
Emelia Asamoah - Early Career Tenure-track Researcher, Morgan State University
Temitope Faleye - Research Fellow, Arizona State University
Tess Pottinger - Research Fellow, Columbia University
Tafadzwa Machipisa - Research Fellow, University of Pennsylvania
Erik Stricker - Graduate Trainee, Baylor College of Medicine
Dottington Fullwood - Research Fellow, Mayo Clinic
Susan Holechek - Project Personnel, Arizona State University
Shahil Pema - Graduate Trainee, Baylor College of Medicine
Scherrayn Garcia - Early Career Tenure-track Researcher, Texas Southern University, College of Pharmacy and Health Sciences
Rose Mary Xavier - Early Career Tenure-track Researcher, University of North Carolina, Chapel Hill
Ioana Voiculescu - Mid-career Tenured Researcher, City University of New York (CUNY)
Purnima Singh - Other, University of Tennessee Health Science Center, Memphis
Pablo Robles Granda - Teacher/Instructor/Professor, University of Illinois at Urbana Champaign
pallavi dubey - Early Career Tenure-track Researcher, Texas Tech University Health Sciences Center at El Paso
Olaoluwa Okusaga - Other, Baylor College of Medicine
Nkechi Mbaezue - Other, Morehouse School of Medicine
Muhammed Idris - Early Career Tenure-track Researcher, Morehouse School of Medicine
Justin Moore - Graduate Trainee, Baylor College of Medicine
Shwetha Kumar - Research Fellow, Baylor College of Medicine
Michaela McCown - Graduate Trainee, Baylor College of Medicine
Madelyn Gillentine - Other, University of Washington
Michelle Bates - Early Career Tenure-track Researcher, Fayetteville State University
Lynette Hammond Gerido - Other, Case Western Reserve University
Latrice Landry - Research Fellow, University of Pennsylvania
Kathryn Shows - Early Career Tenure-track Researcher, Virginia State University
Keesha Roach - Early Career Tenure-track Researcher, University of Tennessee Health Science Center, Memphis
Khadijah Mitchell - Early Career Tenure-track Researcher, Temple University
Kevin Alicea-Torres - Early Career Tenure-track Researcher, University of Puerto Rico Comprehensive Cancer Centre
Kiesha Pierre - Early Career Tenure-track Researcher, Prairie View A&M University
Katelyn Bolhofner - Early Career Tenure-track Researcher, Arizona State University
Kara Malone - Teacher/Instructor/Professor, Baylor College of Medicine
Kosaku Aoyagi - Early Career Tenure-track Researcher, University of Texas at El Paso
Jinyoung Byun - Other, Baylor College of Medicine
Hyunyong Koh - Research Fellow, Baylor College of Medicine
Hamidreza Sharifan - Early Career Tenure-track Researcher, University of Texas at El Paso
Praveen Hariharan - Early Career Tenure-track Researcher, Brown University
Younga Lee - Research Fellow, Mass General Brigham
Grace Tietz - Graduate Trainee, Baylor College of Medicine
Frauke Seemann - Early Career Tenure-track Researcher, Texas A&M University, Corpus Christi
Dipali Rinker - Other, University of Houston
Divita Mathur - Early Career Tenure-track Researcher, Case Western Reserve University
David Blair - Research Fellow, University of California, San Francisco
Cheryl Cropp - Other, Morehouse School of Medicine
Minoo Bagheri - Teacher/Instructor/Professor, Vanderbilt University Medical Center
Jennifer Asmussen - Other, Baylor College of Medicine
Maria Alejandra Gonzalez Gonzalez - Research Fellow, Baylor College of Medicine
Andrew Bigler - Graduate Trainee, Baylor College of Medicine

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ioana Voiculescu - Mid-career Tenured Researcher, City University of New York (CUNY)

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Hamidreza Sharifan - Early Career Tenure-track Researcher, University of Texas at El Paso

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ulanda Simpson - Other, Prairie View A&M University

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Purnima Singh - Other, University of Tennessee Health Science Center, Memphis

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Latrice Landry - Research Fellow, University of Pennsylvania

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yan Ding - Research Fellow, Baylor College of Medicine

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yoruba Mutakabbir - Mid-career Tenured Researcher, Hampton University

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Julie Coleman - Project Personnel, Baylor College of Medicine

Exploring the Association between Hepatitis C Virus and Type 2 Diabetes Mellitus

There is a known association between Hepatitis C virus (HCV) and Type 2 Diabetes Mellitus (T2D) although the direction of the relationship is not fully understood, i.e. whether HCV increases the risk of T2D or if T2D increases the risk…

Scientific Questions Being Studied

There is a known association between Hepatitis C virus (HCV) and Type 2 Diabetes Mellitus (T2D) although the direction of the relationship is not fully understood, i.e. whether HCV increases the risk of T2D or if T2D increases the risk of contracting HCV or both. Some studies suggest that treatment of HCV with direct-acting antivirals attenuates the risk of developing T2D or possibly makes it easier to achieve optimal glucose control in people who already have T2D. This may be because HCV interferes with insulin signaling in liver cells. On the other hand, people known to have T2D first are reportedly at higher risk of developing an HCV infection (versus those people without T2D). This study will attempt to better understand the association and directionality between HCV and T2D. Are people with T2D, versus people with T1D, at higher risk for contracting HCV? Are people with HCV, versus Hepatitis B virus, at higher risk for developing T2D? And does treating the HCV infection help?

Project Purpose(s)

Disease Focused Research (Type 2 Diabetes Mellitus and Hepatitis C Virus)

Scientific Approaches

First, the cross-sectional association between prevalence of HCV and T2D within patients in the All of Us study will be determined compared to the association between Hepatitis B virus infection and a diagnosis of T2D.
Second, the longitudinal directionality of the relationship will be explored. Cohorts of people with either T2D or T1D but no evidence of HCV at baseline will be created and subsequent rates of HCV diagnosis will be determined. Similarly, cohorts of people with either evidence of HCV or Hepatitis B virus infection, but no evidence of T2D, will be created and subsequent rates of T2D diagnosis will be determined. In the group of people with available data, the relationship of HCV viral load or treatment with direct acting antivirals (e.g. sofosbuvir) with subsequent development of T2D or, in people with T2D, the relationship with glucose control (represented by number of glucose-lowering medications used and hemoglobin A1c) will be explored.

Anticipated Findings

It is likely that the previously reported cross-sectional association between T2D and HCV will be confirmed in the All of Us cohort, but prevalence differences by race and sex will be added to the scientific knowledge base. This study will then go on to look at the directionality of this association to better understand whether T2D increases the risk of contracting HCV or whether HCV increases the risk of developing insulin resistance and T2D. It is anticipated that a lower HCV viral load will reduce the risk of developing T2D and that a lower HCV viral load will also be associated with better glucose control (represented by either a reduced number of glucose-lowering medications required and/or a lower average hemoglobin A1c).

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Marie Thearle - Project Personnel, New York City Health & Hospitals

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Purnima Singh - Other, University of Tennessee Health Science Center, Memphis

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Olaoluwa Okusaga - Other, Baylor College of Medicine

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kiesha Pierre - Early Career Tenure-track Researcher, Prairie View A&M University

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Cheryl Cropp - Other, Morehouse School of Medicine

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Keesha Roach - Early Career Tenure-track Researcher, University of Tennessee Health Science Center, Memphis

Deep Metric Learning for Diabetes Subtyping

The International Diabetes Federation estimates that 10% of the world's population will have diabetes by 2035. Patients living with diabetes are at higher risk for many acute and chronic complications, which may lead to increased hospital or ED visits. Accurate…

Scientific Questions Being Studied

The International Diabetes Federation estimates that 10% of the world's population will have diabetes by 2035. Patients living with diabetes are at higher risk for many acute and chronic complications, which may lead to increased hospital or ED visits. Accurate subtyping of those with type 2 diabetes is crucial to understand what characteristics of patients lead to increased risk of adverse outcomes, and is key to more effective and targeted treatments of diabetes and its complications.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Methods Development

Scientific Approaches

We will make use of a machine learning method called Deep Metric Learning (DML). DML seeks to learn a representation of the patient's state by maximizing its similarity with other patients with the same label. DML has previously been shown to be effective in subtyping several diseases in the medical imaging domain. However, DML has not been widely used on Electronic Health Records (EHR) and genetic data. Here, we propose using DML to learn subtypes for type 2 diabetes using time-series data from the EHR, as well as survey and genetic data.

Anticipated Findings

We anticipate that the DML representations we learn will form natural clusters corresponding to patient subtypes. We anticipate that patients in particular subtypes will exhibit similarities based on their input features (i.e. demographics, labs, vitals, surveys, or genetics). Patients in different subtypes may also have different outcomes (i.e. # hospital visits, complications, mortality). We believe that characterizing such subtypes will be useful for clinicians to provide targeted treatments to different patients, which may improve health outcomes. Characterizing such subtypes may also be useful in more accurate diagnosis of diabetes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Qixuan Jin - Graduate Trainee, Massachusetts Institute of Technology
Haoran Zhang - Graduate Trainee, Massachusetts Institute of Technology

Collaborators:

Xuhai "Orson" Xu - Research Fellow, Massachusetts Institute of Technology
Kai Wang - Research Fellow, Massachusetts Institute of Technology
Vinith Suriyakumar - Graduate Trainee, Massachusetts Institute of Technology
Ravi Mandla - Project Personnel, Broad Institute
Eileen Pan - Graduate Trainee, Massachusetts Institute of Technology
Cassandra Parent - Graduate Trainee, Massachusetts Institute of Technology

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Younga Lee - Research Fellow, Mass General Brigham

Duplicate of V7 Introductory example of GWAS with type 2 diabetes phenotype

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Scientific Questions Being Studied

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Project Purpose(s)

Educational

Scientific Approaches

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Anticipated Findings

Not applicable - this workspace is intended to be an introductory example of how to do a genome-wide association study on the All of Us genomic data that individuals can easily click through and understand.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Pablo Robles Granda - Teacher/Instructor/Professor, University of Illinois at Urbana Champaign

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