Michelle Nguyen

Graduate Trainee, Johns Hopkins University

2 active projects

AMIA Genomics Walkthrough - TIRI Lab Sandbox

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Scientific Questions Being Studied

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Project Purpose(s)

  • Educational

Scientific Approaches

We use OMOP, the cohort builder, and Hail for genomics analysis. The approach will be a simple rule based algorithm along with some open access genomic data to simulate a GWAS.

Anticipated Findings

We do not anticipate any findings as the analysis will not be real. Therefore this will not contribute to scientific knowledge, but hopefully will contribute to individual user's knowledge.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Michelle Nguyen - Graduate Trainee, Johns Hopkins University
  • Casey Taylor - Early Career Tenure-track Researcher, Johns Hopkins University

Collaborators:

  • Shanshan Song - Graduate Trainee, Johns Hopkins University
  • Rebecca Yoo - Graduate Trainee, Johns Hopkins University
  • Nidhi Soley - Project Personnel, Johns Hopkins University

Duplicate of AMIA Genomics Walkthrough

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Scientific Questions Being Studied

This workspace is intended to provide basic instruction for using genomics data in All of Us, including cohort identification, covariate extraction, analysis, and display of results.

Project Purpose(s)

  • Educational

Scientific Approaches

We use OMOP, the cohort builder, and Hail for genomics analysis. The approach will be a simple rule based algorithm along with some open access genomic data to simulate a GWAS.

Anticipated Findings

We do not anticipate any findings as the analysis will not be real. Therefore this will not contribute to scientific knowledge, but hopefully will contribute to individual user's knowledge.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

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