Research Projects Directory

Demo - Pharmacogenomics (PGx) variant frequency and medication exposures

1) How common are actionable PGx variants among the diverse participants of different genetic ancestry in AoURP? 2) What percentage of AoU participants are prescribed drugs with PGx prescribing recommendations? Which of these drugs are most common? 3) How often…

Scientific Questions Being Studied

1) How common are actionable PGx variants among the diverse participants of different genetic ancestry in AoURP?
2) What percentage of AoU participants are prescribed drugs with PGx prescribing recommendations? Which of these drugs are most common?
3) How often do participants have gene-drug interactions defined as an actionable phenotype and an exposure to a medication with PGx recommendations. How often will AoU participants receive AoU "DNA and your medication" PGx reports that are relevant to them personally (based on genetic data and prior medication use)?

Project Purpose(s)

Methods Development
Ancestry
Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)

Scientific Approaches

We will assess the AoU variant data for the presence of specific alleles and predicted phenotypes known to be associated with adverse drug reactions or altered dosage recommendations. These data will be also be stratified by genetic ancestry for comparison to gnomAD and CPIC published frequencies. In parallel, frequency of medication exposures to drugs with pharmacogenomic recommendations (CPIC and FDA) will be determined from electronic health record data. Finally, the intersection of these data will provide for estimates of potential utility of pharmacogenomic data among AoURP participants by evaluating gene-predicted phenotype-medication exposures.

Anticipated Findings

We expect these data will show the a potential high impact of PGx broadly and across diverse groups. We expect frequencies to be similar to prior published analyses from academic health systems, gnomAD, and CPIC provide the opportunity for comparison. Genetics ancestry groups will provide data on the frequencies in diverse populations. The data will also be useful for guiding AoU internally regarding the impact of return of results (RoR) for PGx genes currently approved for return (under our IDE) as well as those considered for future addition. This can steer expectations of participant engagement and RoR processes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh

Collaborators:

Andrew Haddad - Graduate Trainee, University of Pittsburgh

Cardiovascular outcomes

There are many clinical, lifestyle, and genetic factors that contribute to different cardiovascular outcomes that have been discovered. Many cohort and case control studies have improved our understanding of cardiovascular disease, however there still remains a significant amount of variability…

Scientific Questions Being Studied

There are many clinical, lifestyle, and genetic factors that contribute to different cardiovascular outcomes that have been discovered. Many cohort and case control studies have improved our understanding of cardiovascular disease, however there still remains a significant amount of variability in patients who experience cardiovascular outcomes and those who do not. We intend to use the All of Us cohort to discover and replicate clinical and genetic associations with cardiovascular outcomes to improve disease prediction.

Project Purpose(s)

Disease Focused Research (arteriosclerotic cardiovascular disease)
Methods Development
Ancestry

Scientific Approaches

We will identify participants with cardiovascular outcomes using available EHR data as cases. Controls will be selected to match cases using propensity score matching. Genetic and clinical factors will be evaluated for association with the outcome.

Anticipated Findings

We anticipate discovery of clinical and genetic variants that are associated with cardiovascular outcomes. We intend to replicate our discoveries in additional cohorts. The findings would provide further information to help guide early therapy for participants who may be particular susceptible to cardiovascular diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh
Andrew Haddad - Graduate Trainee, University of Pittsburgh

Pharmacogenomic haplotype characterization

Pharmacogenomics (PGx) is the application of germline genetics towards precision medicine regarding medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has also developed 26 clinical guidelines for >100 gene-drug pairs. However, there is still considerable interpatient variability in the response to…

Scientific Questions Being Studied

Pharmacogenomics (PGx) is the application of germline genetics towards precision medicine regarding medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has also developed 26 clinical guidelines for >100 gene-drug pairs. However, there is still considerable interpatient variability in the response to medications with adverse drug reactions persisting as a common reason for hospitalization for patients. Additionally, there is high interpatient variability in efficacious response to medications which has been evident in drug classes such as antidepressants and anticoagulants. One reason for this variability could be due to incomplete haplotype characterization of PGx genes. Many variants and haplotypes have been identified, however the definitions are still incomplete. We aim to improve characterization of PGx haplotypes using the diversity of the All of Us cohort.

Project Purpose(s)

Ancestry

Scientific Approaches

We plan to utilize all available genetic data in our analysis. Newly discovered haplotypes will be identified and confirmed using the multiple genotyping approaches that are available. We will also attempt to predict the impact of the newly identified variants and haplotypes.

Anticipated Findings

We anticipate discovery of novel variants and haplotypes as well as improved characterization of known haplotypes in different populations. The findings will be submitted to PGx databases such as PharmVar, CPIC, and PharmGKB to help disseminate the findings.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh
Andrew Haddad - Graduate Trainee, University of Pittsburgh

Warfarin Outcomes v7

Warfarin remains a commonly used anticoagulant despite the introduction of newer treatment options. Dosing, outcomes, and treatment has large variability between patients due to many contributing factors. Therefore our question is: What are the factors that influence outcomes related to…

Scientific Questions Being Studied

Warfarin remains a commonly used anticoagulant despite the introduction of newer treatment options. Dosing, outcomes, and treatment has large variability between patients due to many contributing factors. Therefore our question is: What are the factors that influence outcomes related to warfarin treatment?

Project Purpose(s)

Population Health
Ancestry

Scientific Approaches

We aim to use electronic health record (EHR) data, demographic information, and genetic data to build statistical models which can help to improve outcomes related to warfarin.

Anticipated Findings

We anticipate developing models in a diverse cohort which can improve outcomes related to warfarin.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh
Andrew Haddad - Graduate Trainee, University of Pittsburgh

Demo - Pharmacogenomics (PGx) variant frequency and medication exposures

1) How common are actionable PGx variants among the diverse participants of different genetic ancestry in AoURP? 2) What percentage of AoU participants are prescribed drugs with PGx prescribing recommendations? Which of these drugs are most common? 3) How often…

Scientific Questions Being Studied

1) How common are actionable PGx variants among the diverse participants of different genetic ancestry in AoURP?
2) What percentage of AoU participants are prescribed drugs with PGx prescribing recommendations? Which of these drugs are most common?
3) How often do participants have gene-drug interactions defined as an actionable phenotype and an exposure to a medication with PGx recommendations. How often will AoU participants receive AoU "DNA and your medication" PGx reports that are relevant to them personally (based on genetic data and prior medication use)?

Project Purpose(s)

Methods Development
Ancestry
Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use.)

Scientific Approaches

We will assess the AoU variant data for the presence of specific alleles and predicted phenotypes known to be associated with adverse drug reactions or altered dosage recommendations. These data will be also be stratified by genetic ancestry for comparison to gnomAD and CPIC published frequencies. In parallel, frequency of medication exposures to drugs with pharmacogenomic recommendations (CPIC and FDA) will be determined from electronic health record data. Finally, the intersection of these data will provide for estimates of potential utility of pharmacogenomic data among AoURP participants by evaluating gene-predicted phenotype-medication exposures.

Anticipated Findings

We expect these data will show the a potential high impact of PGx broadly and across diverse groups. We expect frequencies to be similar to prior published analyses from academic health systems, gnomAD, and CPIC provide the opportunity for comparison. Genetics ancestry groups will provide data on the frequencies in diverse populations. The data will also be useful for guiding AoU internally regarding the impact of return of results (RoR) for PGx genes currently approved for return (under our IDE) as well as those considered for future addition. This can steer expectations of participant engagement and RoR processes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh

Collaborators:

Andrew Haddad - Graduate Trainee, University of Pittsburgh

pgrn-hail-gwas demo

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Scientific Questions Being Studied

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Project Purpose(s)

Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)

Scientific Approaches

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Anticipated Findings

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh

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