Research Projects Directory

Research Projects Directory

14,430 active projects

This information was updated 11/21/2024

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. Views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program. Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

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6 projects have 'precision health outcomes realized' in the project title
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Precision Health Outcomes Realized - PHOuR Breast

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Eric Mischo - Project Personnel, University of Wisconsin, Madison
  • Elizabeth Burnside - Mid-career Tenured Researcher, University of Wisconsin, Madison
  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Yuchang Wu - Research Fellow, University of Wisconsin, Madison
Request a Review of this Research Project

Precision Health Outcomes Realized - PHOuR Breast - v6

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Inês Dutra - Late Career Tenured Researcher, University of Wisconsin, Madison
  • Josie Hintzke - Project Personnel, University of Wisconsin, Madison
  • Aubrey Barnard - Research Fellow, University of Wisconsin, Madison
  • Eric Mischo - Project Personnel, University of Wisconsin, Madison
  • Elizabeth Burnside - Mid-career Tenured Researcher, University of Wisconsin, Madison
  • Yuchang Wu - Research Fellow, University of Wisconsin, Madison
Request a Review of this Research Project

Precision Health Outcomes Realized (PHOuR) Breast Project-Original

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Disease Focused Research (breast cancer)
  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Eric Mischo - Project Personnel, University of Wisconsin, Madison
  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Yeonhee Park - Early Career Tenure-track Researcher, University of Wisconsin, Madison
Request a Review of this Research Project

Precision Health Outcomes Realized (PHOuR) Breast Project

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Disease Focused Research (breast cancer)
  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Eric Mischo - Project Personnel, University of Wisconsin, Madison
  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Yeonhee Park - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Josie Hintzke - Project Personnel, University of Wisconsin, Madison
Request a Review of this Research Project

Precision Health Outcomes Realized - PHOuR Breast - v7

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Yuchang Wu - Research Fellow, University of Wisconsin, Madison
  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Inês Dutra - Late Career Tenured Researcher, University of Wisconsin, Madison
  • Elizabeth Burnside - Mid-career Tenured Researcher, University of Wisconsin, Madison
  • Aubrey Barnard - Research Fellow, University of Wisconsin, Madison
Request a Review of this Research Project

Duplicate of Precision Health Outcomes Realized - PHOuR Breast - v7DUP

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based…

Scientific Questions Being Studied

Despite advances in breast cancer screening, prevention, and treatment, over 40,000 women still die of breast cancer each year in the United States. Growing interest in risk-based screening creates an urgent mandate to determine the effectiveness of a personalized, risk-based approach to breast cancer screening. A pivotal factor for improving breast cancer risk prediction is determining the maximum predictive power that can be obtained by using more explanatory genetic variants combined with variables extracted from data inherent in electronic health records (EHR). Analytics using genetic variants and intermediate phenotypes like mammographic breast density and EHR variables have the potential to augment existing risk based models. The project is designed to harness the power of predictive modeling to enable personalized, tailored screening protocols with the ultimate goal of improving breast cancer outcomes for women.

Project Purpose(s)

  • Methods Development

Scientific Approaches

This project will develop and refine a new model for estimating breast cancer risk using genetic variants (single nucleotide polymorphisms-SNPs) combined with electronic health record (EHR) variables to inform polygenic risk scores (PRSs). The study will employ a standardized format (Observational Medical Outcomes Partnership), which provides a framework for translating data from disparate coding systems to a standardized vocabulary. We will extract variables from the All of Us data. The extracted variables will be used to obtain a parsimonious set of variables identified to be most strongly associated with breast cancer. We will determine the most important SNPs contributing to PRSs and develop a power calculation. We will then test the model and demonstrate proof of principle when applied to an internal/local dataset. The model’s performance will be gauged by positive predictive value, negative predictive value, sensitivity, specificity and area under the ROC curve.

Anticipated Findings

This project aims to develop advanced algorithms to contribute to personalized approaches to breast cancer screening. We anticipate the ability to stratify risk by examining variables and data points that may not be readily observable, but interact with genetics to predict future outcomes. Genome-wide association studies (GWAS) have detected multiple genetic variants associated with breast cancer risk. Typically, GWAS techniques identify straightforward statistical associations between SNPs and diseases rather than leveraging biological mechanisms or SNP interactions. Risk models using high dimensional variables, EHR data, SNPs, and intermediate phenotypes like mammographic breast density, have the potential to improve risk stratification. Implementation of these advanced models will contribute to a clinical paradigm that uses knowledge gained from analyzing genomic sequence data and/or other large scale datasets to improve breast cancer outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Yuchang Wu - Research Fellow, University of Wisconsin, Madison
  • Qiongshi Lu - Early Career Tenure-track Researcher, University of Wisconsin, Madison
  • Inês Dutra - Late Career Tenured Researcher, University of Wisconsin, Madison
  • Elizabeth Burnside - Mid-career Tenured Researcher, University of Wisconsin, Madison
  • Aubrey Barnard - Research Fellow, University of Wisconsin, Madison
Request a Review of this Research Project
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