QiPing Feng

Early Career Tenure-track Researcher, Vanderbilt University Medical Center

10 active projects

Version 7 of Genetics of infections and sepsis

Sepsis, an outcome of severe infection, is present in 30-50% of hospitalizations that culminate in death and is the single most expensive medical condition, accounting for 13% of total US hospital costs. There is marked inter-individual variability in susceptibility to…

Scientific Questions Being Studied

Sepsis, an outcome of severe infection, is present in 30-50% of hospitalizations that culminate in death and is the single most expensive medical condition, accounting for 13% of total US hospital costs. There is marked inter-individual variability in susceptibility to infection and progression to sepsis and death. Susceptibility to infection is highly heritable, likely due to major selection pressure over millennia when infection was the leading cause of death, and no effective treatments existed. Surprisingly, there is almost no large-scale information about the genetic determinants of 1) severe infection, 2) sepsis, and 3) death from sepsis.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

We will identify individuals with infection and matched controls without infection. Within the cohort of patients with infection, we will identify those who developed sepsis and who died in the hospital. We will apply genetic approaches to identify the genetic determinants of susceptibility to severe infection, sepsis and its most serious complication, death.

Anticipated Findings

Findings from the proposed study will define the underlying mechanisms of sepsis and may reveal new candidates for early detection, prevention, and treatment of the progression from infection to sepsis and from sepsis to death.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Lan Jiang - Other, Vanderbilt University Medical Center

Collaborators:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center

candidate meds_triglycerides and genetics

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the…

Scientific Questions Being Studied

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the US. Plasma triglyceride (TG) levels are a strong predictor for CHD even after LDL-C lowering. The status quo is that most TG-lowering drugs in development focus on the LPL pathway--we need to identify new TG targets in other pathways.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will identify individuals with Triglycerides (TG) measurement and genetic information. We will conduct genetic analyses to identify novel genetic variants associated with TG levels.

Anticipated Findings

We anticipate identifying genetic variations associated with TG levels. We anticipate understanding pleiotropic effects of the candidate TG medications

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Yi Xin - Graduate Trainee, Vanderbilt University Medical Center
  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • Lan Jiang - Other, Vanderbilt University Medical Center
  • Elliot Outland - Project Personnel, Vanderbilt University Medical Center
  • Alyson Dickson - Project Personnel, Vanderbilt University Medical Center

version 7 dup of Genetics and Triglycerides

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the…

Scientific Questions Being Studied

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the US. Plasma triglyceride (TG) levels are a strong predictor for CHD even after LDL-C lowering. The status quo is that most TG-lowering drugs in development focus on the LPL pathway--we need to identify new TG targets in other pathways.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

We will identify individuals with Triglycerides (TG) measurement and genetic information. We will conduct genetic analyses to identify novel genetic variants associated with TG levels.

Anticipated Findings

We anticipate identifying genetic variations associated with TG levels.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Lan Jiang - Other, Vanderbilt University Medical Center
  • Elliot Outland - Project Personnel, Vanderbilt University Medical Center

Collaborators:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Jun Qian - Other, All of Us Program Operational Use
  • Alyson Dickson - Project Personnel, Vanderbilt University Medical Center

OLD_Genetics and Triglycerides_OLD

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the…

Scientific Questions Being Studied

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the US. Plasma triglyceride (TG) levels are a strong predictor for CHD even after LDL-C lowering. The status quo is that most TG-lowering drugs in development focus on the LPL pathway--we need to identify new TG targets in other pathways.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will identify individuals with Triglycerides (TG) measurement and genetic information. We will conduct genetic analyses to identify novel genetic variants that associated with TG levels.

Anticipated Findings

We anticipate identifying novel genes associated with TGs. These findings will further our understanding of TG regulation and may lead to novel TG-lowering targets.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Lan Jiang - Other, Vanderbilt University Medical Center

Genetics and Triglycerides

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the…

Scientific Questions Being Studied

Coronary heart disease (CHD) accounts for 1 in 7 deaths in the US and 8.2 million Americans ≥20 years old have CHD. Despite intensive treatment to lower low-density lipoprotein cholesterol (LDL-C), CHD remains the leading cause of death in the US. Plasma triglyceride (TG) levels are a strong predictor for CHD even after LDL-C lowering. The status quo is that most TG-lowering drugs in development focus on the LPL pathway--we need to identify new TG targets in other pathways.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

We will identify individuals with Triglycerides (TG) measurement and genetic information. We will conduct genetic analyses to identify novel genetic variants associated with TG levels.

Anticipated Findings

We anticipate identifying genetic variations associated with TG levels.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Lan Jiang - Other, Vanderbilt University Medical Center
  • Elliot Outland - Project Personnel, Vanderbilt University Medical Center

Collaborators:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Alyson Dickson - Project Personnel, Vanderbilt University Medical Center

Exploring_AOU_Data

This study will identify patients tested COVID positive to identify conditions occurring after covid-19. We would like to explore genomic and fitbit data and see if they add any value in determining the status of long covid.

Scientific Questions Being Studied

This study will identify patients tested COVID positive to identify conditions occurring after covid-19. We would like to explore genomic and fitbit data and see if they add any value in determining the status of long covid.

Project Purpose(s)

  • Disease Focused Research (long covid-19)

Scientific Approaches

This project uses Recover algorithm to identify patients with long-covid.
We are using xgboost libraries to run the model developed by n3c.

Anticipated Findings

We plan to study how different data like fitbit and genomic data contribute towards the patient having or not having long covid.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • Elliot Outland - Project Personnel, Vanderbilt University Medical Center

Genetics of infections and sepsis

Sepsis, an outcome of severe infection, is present in 30-50% of hospitalizations that culminate in death and is the single most expensive medical condition, accounting for 13% of total US hospital costs. There is marked inter-individual variability in susceptibility to…

Scientific Questions Being Studied

Sepsis, an outcome of severe infection, is present in 30-50% of hospitalizations that culminate in death and is the single most expensive medical condition, accounting for 13% of total US hospital costs. There is marked inter-individual variability in susceptibility to infection and progression to sepsis and death. Susceptibility to infection is highly heritable, likely due to major selection pressure over millennia when infection was the leading cause of death, and no effective treatments existed. Surprisingly, there is almost no large-scale information about the genetic determinants of 1) severe infection, 2) sepsis, and 3) death from sepsis.

Project Purpose(s)

  • Population Health
  • Ancestry

Scientific Approaches

We will identify individuals with infection and matched controls without infection. Within the cohort of patients with infection, we will identify those who developed sepsis and who died in the hospital. We will apply genetic approaches to identify the genetic determinants of susceptibility to severe infection, sepsis and its most serious complication, death.

Anticipated Findings

Findings from the proposed study will define the underlying mechanisms of sepsis and may reveal new candidates for early detection, prevention, and treatment of the progression from infection to sepsis and from sepsis to death.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Lan Jiang - Other, Vanderbilt University Medical Center

Duplicate of Demo - Siloed Analysis of All of Us and UK Biobank Genomic Data

Historically, researchers responded to limitations in genomic data sharing policy and practice by conducting meta analysis on summary outputs from isolated genomic datasets. Recent work has demonstrated the increased power of individual-level genetic analysis on pooled datasets. In addition, advancements…

Scientific Questions Being Studied

Historically, researchers responded to limitations in genomic data sharing policy and practice by conducting meta analysis on summary outputs from isolated genomic datasets. Recent work has demonstrated the increased power of individual-level genetic analysis on pooled datasets. In addition, advancements in data access and sharing policies coupled with technological advancements in cloud-based environments for data access and analysis have opened up new possibilities for pooled analysis of large-scale genomic datasets. The NIH All of Us Research Program and UK Biobank are two leading examples of large, population scale studies which combine genomic data with deep phenotypic health data. There is a grand opportunity to demonstrate how the world’s largest research-ready biomedical datasets can create more value together and advance discovery in genome science.

Project Purpose(s)

  • Other Purpose (This is a demonstration project meant to support research with All of Us Genomic Data)

Scientific Approaches

The primary goal of this project is to demonstrate the potential of the All of Us Researcher Workbench for pooled analyses of All of Us and UK Biobank data. Specifically, we aim to: 1. Develop and describe an approved, secure path for connecting UK Biobank data to the All of Us Researcher Workbench. 2. Conduct a genome-wide association study of blood lipids on the pooled dataset aimed at demonstrating that biomedical researchers can be more productive when permitted to analyze the union of the cohorts, as opposed to computing aggregate results in separate data silos for each cohort and then combining those aggregates.

Anticipated Findings

The secondary goal of this project is to demonstrate and measure the experience when the same analyses are repeated in a siloed manner. Specifically we aim to: 3. Repeat the previously described genome-wide association study on the All of Us Researcher Workbench when working with the All of Us data and on UK Biobank’s DNAnexus when working with the UK Biobank data. 4. Conduct a meta analysis on the aggregate results for each cohort (in accordance with each program’s data use policies) and compare the result of combining those aggregates to the results from the pooled analysis. Evaluate not only differences in results, but also differences in analysis cost and analyst productivity.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Duplicate of How to Work with All of Us Genomic Data (Hail - Plink)(v6)

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:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center

Duplicate of How to Get Started with Controlled Tier Data

1. Socio-Economic Metrics: How to retrieve participants' socio-economic data from the CDR. 2. Observation Date: How to query and plot an observation date using survey completion date as example. 3. Demographics: Examples of how to query and plot participant demographic…

Scientific Questions Being Studied

1. Socio-Economic Metrics: How to retrieve participants' socio-economic data from the CDR.
2. Observation Date: How to query and plot an observation date using survey completion date as example.
3. Demographics: Examples of how to query and plot participant demographic data.
4. Death Cause: How to retrieve and plot deceased participants' death causes.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Featured Workspace: - teaches the users how to set up this notebook, install and import software packages, and select the correct version of the CDR. - gives an overview of the data types available in the current Controlled Tier Curated Data Repository (CDR) that are not available in the Registered Tier - shows how to retrieve and summarize this data.)

Scientific Approaches

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. The tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). It contains helper functions for repeatedly, code readability and efficiency and repeatedly.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following: All of Us data are made available in two Curated Data Repository: the Registered Tier and Controlled Tier. The latter was subject to more relaxed privacy rules relative to the Registered Tier. As a result, you can expect to find more concept ids in certain data types such as EHR and Survey.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • QiPing Feng - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
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