Jeffrey Haessler

Project Personnel, Fred Hutchinson Cancer Research Center

6 active projects

Duplicate of Type 2 Diabetes and Lipids PRS Current

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated…

Scientific Questions Being Studied

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated screening for those at high risk, or even guiding therapeutic interventions. The All of Us Research Program is uniquely situated to address these questions and identify opportunities for tailoring prediction across multi-ancestry populations.

Our primary study question is to investigate whether a multi-ancestry T2D PRS will be associated with T2D-related phenotypic risk factors and complications across diverse populations. In particular, we hypothesize that the T2D PRS may help identify individuals at greater risk of developing T2D-related complications. Our secondary study question will investigate whether the effect of the T2D PRS on T2D risk is mediated by T2D risk factors.

Project Purpose(s)

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

Scientific Approaches

We will construct a multi-ancestry T2D PRS based on a large multi-ancestry T2D GWAS meta-analysis. We will also construct a PRS based on a recent T2D GWAS meta-analysis that includes a larger number of non-European ancestry individuals. In addition, we will evaluate a genome-wide PRS using recent approaches such as LDPred2, PRS-CSx, and others.

For our primary study question, in analyses stratified by T2D status, we will evaluate the association between the T2D PRS and the T2D-related risk factors and complications using regression models. For our secondary study question, we will evaluate whether the T2D risk factors mediate the effect of the T2D PRS on T2D status. This will be performed using regression models to assess the relationship between 1) the PRS and T2D risk, 2) the PRS and the T2D risk factor, 3) the T2D risk factor and T2D risk, and 4) the PRS and T2D risk while adjusting for the T2D risk factor.

Anticipated Findings

We hypothesize that our T2D PRS may hold prognostic value and help identify individuals at greater risk of developing T2D-related complications. We anticipate that the effectiveness of T2D PRS is context-dependent. The T2D PRS may perform well in predicting T2D risk for diverse populations and may hold potential prognostic value for prediabetes and T2D controls.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jeffrey Haessler - Project Personnel, Fred Hutchinson Cancer Research Center
  • Harriett Fuller - Research Fellow, Fred Hutchinson Cancer Research Center
  • Boya Guo - Graduate Trainee, Fred Hutchinson Cancer Research Center
  • Burcu Darst - Early Career Tenure-track Researcher, Fred Hutchinson Cancer Research Center

Duplicate of Phenotype - Ischemic Heart Disease (v7)

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Scientific Questions Being Studied

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR)..)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Christianne L. Roumie; Jana Shirey-Rice, Sunil Kripalani. Vanderbilt University. MidSouth CDRN - Coronary Heart Disease Algorithm. PheKB; 2014. Available from https://phekb.org/phenotype/234

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Type 2 Diabetes and Lipids PRS

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated…

Scientific Questions Being Studied

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated screening for those at high risk, or even guiding therapeutic interventions. The All of Us Research Program is uniquely situated to address these questions and identify opportunities for tailoring prediction across multi-ancestry populations.

Our primary study question is to investigate whether a multi-ancestry T2D PRS will be associated with T2D-related phenotypic risk factors and complications across diverse populations. In particular, we hypothesize that the T2D PRS may help identify individuals at greater risk of developing T2D-related complications. Our secondary study question will investigate whether the effect of the T2D PRS on T2D risk is mediated by T2D risk factors.

Project Purpose(s)

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

Scientific Approaches

We will construct a multi-ancestry T2D PRS based on a large multi-ancestry T2D GWAS meta-analysis. We will also construct a PRS based on a recent T2D GWAS meta-analysis that includes a larger number of non-European ancestry individuals. In addition, we will evaluate a genome-wide PRS using recent approaches such as LDPred2, PRS-CSx, and others.

For our primary study question, in analyses stratified by T2D status, we will evaluate the association between the T2D PRS and the T2D-related risk factors and complications using regression models. For our secondary study question, we will evaluate whether the T2D risk factors mediate the effect of the T2D PRS on T2D status. This will be performed using regression models to assess the relationship between 1) the PRS and T2D risk, 2) the PRS and the T2D risk factor, 3) the T2D risk factor and T2D risk, and 4) the PRS and T2D risk while adjusting for the T2D risk factor.

Anticipated Findings

We hypothesize that our T2D PRS may hold prognostic value and help identify individuals at greater risk of developing T2D-related complications. We anticipate that the effectiveness of T2D PRS is context-dependent. The T2D PRS may perform well in predicting T2D risk for diverse populations and may hold potential prognostic value for prediabetes and T2D controls.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Workshop: Intro to All of Us Genomics Data

This workspace is meant to help researchers get familiar with the All of Us Researcher Workbench. There are five hands-on exercises during the workshop, each with a specific notebook. Exercise 1: Duplicate the workspace & start the cloud environment Exercise…

Scientific Questions Being Studied

This workspace is meant to help researchers get familiar with the All of Us Researcher Workbench. There are five hands-on exercises during the workshop, each with a specific notebook.
Exercise 1: Duplicate the workspace & start the cloud environment
Exercise 2: Looking at the genomic data (notebook)
Exercise 3: GWAS - extracting phenotypic data (notebook)
Exercise 4: GWAS - running Hail GWAS (notebook)
Exercise 5: Advanced GWAS (2 notebooks)

By running the exercises in this workspace, researchers will become more familiar with the genomic data, know how to access the genomic data, see how the genomic data and tools can be used in the Researcher Workbench, and be able to start their own genomic data project.

Project Purpose(s)

  • Other Purpose (This workspace is meant for use during the Introduction to Analyzing All of Us Genomic Data workshop. In this workshop, participants will get hands-on experience using the genomics data running a genome-wide association study (GWAS) using Hail. )

Scientific Approaches

We are using the All of Us dataset in order to run a genome-wide association study (GWAS) using Hail. In the workshop, we will give an introduction to the All of Us Researcher Workbench and demonstrate how to use the Cohort Builder and Jupyter Notebooks to set up a research project. Using Jupyter notebooks, we will create a dataset linking the All of Us phenotypic data to the short read whole genome sequencing (srWGS) data. After running the GWAS steps using Hail, we will visualize the results.

Anticipated Findings

This study is running a genome-wide association study (GWAS) using Hail, using height as the selected phenotypic data. We do not anticipate findings from this example workspace but we expect that workshop participants will be able to apply similar methods to their future research.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Type 2 Diabetes and Lipids PRS v7

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated…

Scientific Questions Being Studied

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated screening for those at high risk, or even guiding therapeutic interventions. The All of Us Research Program is uniquely situated to address these questions and identify opportunities for tailoring prediction across multi-ancestry populations.

Our primary study question is to investigate whether a multi-ancestry T2D PRS will be associated with T2D-related phenotypic risk factors and complications across diverse populations. In particular, we hypothesize that the T2D PRS may help identify individuals at greater risk of developing T2D-related complications. Our secondary study question will investigate whether the effect of the T2D PRS on T2D risk is mediated by T2D risk factors.

Project Purpose(s)

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

Scientific Approaches

We will construct a multi-ancestry T2D PRS based on a large multi-ancestry T2D GWAS meta-analysis. We will also construct a PRS based on a recent T2D GWAS meta-analysis that includes a larger number of non-European ancestry individuals. In addition, we will evaluate a genome-wide PRS using recent approaches such as LDPred2, PRS-CSx, and others.

For our primary study question, in analyses stratified by T2D status, we will evaluate the association between the T2D PRS and the T2D-related risk factors and complications using regression models. For our secondary study question, we will evaluate whether the T2D risk factors mediate the effect of the T2D PRS on T2D status. This will be performed using regression models to assess the relationship between 1) the PRS and T2D risk, 2) the PRS and the T2D risk factor, 3) the T2D risk factor and T2D risk, and 4) the PRS and T2D risk while adjusting for the T2D risk factor.

Anticipated Findings

We hypothesize that our T2D PRS may hold prognostic value and help identify individuals at greater risk of developing T2D-related complications. We anticipate that the effectiveness of T2D PRS is context-dependent. The T2D PRS may perform well in predicting T2D risk for diverse populations and may hold potential prognostic value for prediabetes and T2D controls.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Charles Kooperberg - Late Career Tenured Researcher, Fred Hutchinson Cancer Research Center
  • Jeffrey Haessler - Project Personnel, Fred Hutchinson Cancer Research Center
  • Boya Guo - Graduate Trainee, Fred Hutchinson Cancer Research Center
  • Burcu Darst - Early Career Tenure-track Researcher, Fred Hutchinson Cancer Research Center

Type 2 Diabetes and Lipids PRS

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated…

Scientific Questions Being Studied

It has been estimated that 10.2% of the global population will have diabetes by 2030. PRS combined with non-genetic predictors potentially holds utility for identifying individuals at high risk prior to disease onset, motivating positive health behavior change or repeated screening for those at high risk, or even guiding therapeutic interventions. The All of Us Research Program is uniquely situated to address these questions and identify opportunities for tailoring prediction across multi-ancestry populations.

Our primary study question is to investigate whether a multi-ancestry T2D PRS will be associated with T2D-related phenotypic risk factors and complications across diverse populations. In particular, we hypothesize that the T2D PRS may help identify individuals at greater risk of developing T2D-related complications. Our secondary study question will investigate whether the effect of the T2D PRS on T2D risk is mediated by T2D risk factors.

Project Purpose(s)

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

Scientific Approaches

We will construct a multi-ancestry T2D PRS based on a large multi-ancestry T2D GWAS meta-analysis. We will also construct a PRS based on a recent T2D GWAS meta-analysis that includes a larger number of non-European ancestry individuals. In addition, we will evaluate a genome-wide PRS using recent approaches such as LDPred2, PRS-CSx, and others.

For our primary study question, in analyses stratified by T2D status, we will evaluate the association between the T2D PRS and the T2D-related risk factors and complications using regression models. For our secondary study question, we will evaluate whether the T2D risk factors mediate the effect of the T2D PRS on T2D status. This will be performed using regression models to assess the relationship between 1) the PRS and T2D risk, 2) the PRS and the T2D risk factor, 3) the T2D risk factor and T2D risk, and 4) the PRS and T2D risk while adjusting for the T2D risk factor.

Anticipated Findings

We hypothesize that our T2D PRS may hold prognostic value and help identify individuals at greater risk of developing T2D-related complications. We anticipate that the effectiveness of T2D PRS is context-dependent. The T2D PRS may perform well in predicting T2D risk for diverse populations and may hold potential prognostic value for prediabetes and T2D controls.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Charles Kooperberg - Late Career Tenured Researcher, Fred Hutchinson Cancer Research Center
  • Jeffrey Haessler - Project Personnel, Fred Hutchinson Cancer Research Center
  • Boya Guo - Graduate Trainee, Fred Hutchinson Cancer Research Center
  • Burcu Darst - Early Career Tenure-track Researcher, Fred Hutchinson Cancer Research Center
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