Research Projects Directory

Research Projects Directory

3,097 active projects

This information was updated 11/27/2022

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.

6 projects have 'sealock' in the project owner - last name
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antidepressant response genetics

In this study, we will ask: Does genetic risk for psychiatric traits associate with worse response to antidepressants? Next, we will run a genome-wide association scan to determine if any variants associate with antidepressant response.

Scientific Questions Being Studied

In this study, we will ask: Does genetic risk for psychiatric traits associate with worse response to antidepressants? Next, we will run a genome-wide association scan to determine if any variants associate with antidepressant response.

Project Purpose(s)

  • Disease Focused Research (Psychiatric Disorders)

Scientific Approaches

We will calculate polygenic scores using either PLINK or PRS-CS. We will use SAIGE or Ordinal GWAS to conduct the GWAS.

Anticipated Findings

We anticipate that increased depression PGS associates with worse antidepressant response. We hope the GWAS will yield genome-wide significant variants.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Antidepressant Response Datav6

Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or…

Scientific Questions Being Studied

Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or drug combination that works for them. EHRs provide detailed information on the medications individuals take, however, it is not always clear in an EHR how well a patient responds to a particular medication. Sometimes, physicians will administer a survey to a patient that aims to quantify their depression symptoms (patient health questionnaire, or PHQ). The responses to the PHQ are stored in EHRs. At Vanderbilt, we developed an algorithm that aims to infer antidepressant treatment response based on drug switching. We hope to implement our algorithm in the All of Us data and then use PHQ responses to validate how well our response variables track with survey questions on depression.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder)

Scientific Approaches

Our approach is to implement our drug switching algorithm for antidepressants and then use PHQ outcomes to determine how well our response outcome tracks with depression symptoms. This will require longitudinal data on antidepressants and PHQ responses.

Anticipated Findings

We hope that our algorithm will be a valid proxy for treatment response that can help increases sample sizes in antidepressant response studies. Future studies could integrate genetic information to determine if there are genetic variants contributing to treatment response. Overall, we hope the algorithm can be used by other EHR researchers and can serve as a paradigm for future treatment response algorithms for other medications.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

URVs in SCZ

The main question for this workspace is, "do ultra rare damaging genetic variants associate with schizophrenia diagnosis?". This question is important because it can help shed light on the biology of schizophrenia to lead to new diagnostic methods and treatments.

Scientific Questions Being Studied

The main question for this workspace is, "do ultra rare damaging genetic variants associate with schizophrenia diagnosis?". This question is important because it can help shed light on the biology of schizophrenia to lead to new diagnostic methods and treatments.

Project Purpose(s)

  • Disease Focused Research (Schizophrenia)
  • Ancestry

Scientific Approaches

First, I will determine how many individuals with WGS data also have a diagnosis of schizophrenia. Next, I will use methods outlined in Singh et al Nature 2022 to find ultra rare damaging variants. Finally, I will run statistical analyses to determine if these variants associate with schizophrenia status.

Anticipated Findings

I anticipate to find an excess of rare variants in schizophrenia cases, and to replicate gene findings from Singh et al 2022.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

mdCAD

The scientific question we intend to study is the relationship between depression and cardiovascular disease outcomes. Cardiovascular disease and depression are commonly co-morbid. Understanding the prevalence and longitudinal relationship between the two diagnoses can help inform future disease monitoring tools…

Scientific Questions Being Studied

The scientific question we intend to study is the relationship between depression and cardiovascular disease outcomes. Cardiovascular disease and depression are commonly co-morbid. Understanding the prevalence and longitudinal relationship between the two diagnoses can help inform future disease monitoring tools and therapeutics.

Project Purpose(s)

  • Disease Focused Research (Depression and Cardiovascular Disease)

Scientific Approaches

We plan to use phecodes for depression, coronary artery disease, and cardiomyopathy to calculate the prevalence of co-occurring depression and heart disease. We also plan to use sex and race information to stratify analyses.

Anticipated Findings

We anticipate to find that adverse cardiovascular events such as cardiomyopathy tend to occur more frequently in individuals with co-morbid depression and CAD compared to individuals with only depression or CAD. Our findings would contribute to a growing body of knowledge on the increased rate of heart disease among depression cases and motivate future studies into the shared biology between depression and heart disease.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

Antidepressant Response

Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or…

Scientific Questions Being Studied

Treatment of major depressive disorder typically begins with antidepressants. Response to antidepressants is highly variable. Around half of individuals will not respond to the first antidepressant they are prescribed, starting a long treatment odyssey to find the a drug or drug combination that works for them. EHRs provide detailed information on the medications individuals take, however, it is not always clear in an EHR how well a patient responds to a particular medication. Sometimes, physicians will administer a survey to a patient that aims to quantify their depression symptoms (patient health questionnaire, or PHQ). The responses to the PHQ are stored in EHRs. At Vanderbilt, we developed an algorithm that aims to infer antidepressant treatment response based on drug switching. We hope to implement our algorithm in the All of Us data and then use PHQ responses to validate how well our response variables track with survey questions on depression.

Project Purpose(s)

  • Disease Focused Research (major depressive disorder)

Scientific Approaches

Our approach is to implement our drug switching algorithm for antidepressants and then use PHQ outcomes to determine how well our response outcome tracks with depression symptoms. This will require longitudinal data on antidepressants and PHQ responses.

Anticipated Findings

We hope that our algorithm will be a valid proxy for treatment response that can help increases sample sizes in antidepressant response studies. Future studies could integrate genetic information to determine if there are genetic variants contributing to treatment response. Overall, we hope the algorithm can be used by other EHR researchers and can serve as a paradigm for future treatment response algorithms for other medications.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Liver Transplant

Our specific questions are about if MELDNa labs contribute to sex differences seen in liver transplant. We plan to use lab values (creatinine, INR, bilirubin, and sodium) to reconstruct MELDNa scores and compare lab and MELDNa values between sexes within…

Scientific Questions Being Studied

Our specific questions are about if MELDNa labs contribute to sex differences seen in liver transplant. We plan to use lab values (creatinine, INR, bilirubin, and sodium) to reconstruct MELDNa scores and compare lab and MELDNa values between sexes within control, liver disease cases, and transplant recipients. We next plan to build a sex-adjusted MELDNa score in conjunction with VUMC.

Project Purpose(s)

  • Disease Focused Research (Liver Transplant)

Scientific Approaches

We plan to use lab values, ICD codes, and CPT codes to define lab traits, liver disease, and liver transplant.

Anticipated Findings

Our findings will contribute to the body of scientific knowledge by showing that all labs in the MELDNa score show sex differences that contribute to lower MELDNa score in females. A sex-adjusted MELDNa score will help close the gap between males and females in transplant.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
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