Yaomin Xu

Early Career Tenure-track Researcher, Vanderbilt University Medical Center

2 active projects

TBILab Learning 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)

  • Educational
  • 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:

  • Yaomin Xu - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • Yajing Li - Project Personnel, Vanderbilt University Medical Center
  • Brian Sharber - Project Personnel, Vanderbilt University Medical Center

Multivariate analysis of phenome, genome relationships

Our overarching hypothesis is that multivariate characterization of complex cross-phenome, cross-genome, phenome-genome relationships can add sensitive, novel toolsets to the biobank data analyses and have profound impact on biobank-based data research applications. The vast majority of human diseases are multifactorial…

Scientific Questions Being Studied

Our overarching hypothesis is that multivariate characterization of complex cross-phenome, cross-genome, phenome-genome relationships can add sensitive, novel toolsets to the biobank data analyses and have profound impact on biobank-based data research applications. The vast majority of human diseases are multifactorial and complex, and their impacts are widespread and often damaging. Leveraging population-wide data to analyze disease relationships and their multivariate patterns is a promising approach to facilitate our understanding of complex disease etiology and help build tools to assess an individual's disease risk. All of US provides unprecedented opportunities for comprehensive and rigorous quantification of multivariate disease relationships in a large real-world population. In this study, we will investigate and build a set of robust multivariate statistical techniques and tools to analyze the phenome-genome wide relationships and identify indications to precision therapy.

Project Purpose(s)

  • Educational
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

Aim1: Quantify phenome-wide disease multimorbidity, validate and interprete in relevant clinical context. We will quantify multimorbidity as two or more diseases co-occurring more frequently than expected. Characterize it's dynamics using multivariate model such as a network model. Compared with those estimated from other data sources. An expert consensus approach will also be used to evaluate the estimated multimorbidity in case studies implemented with our collaborators
Aim 2: Explore multivariate modeling strategy to phenome-genome relationships. Derive systems-level view of disease molecular mechanisms and build a disease multimorbidity patterns based on genetic associations; Build systems-level discovery strategies for disease pleiotropic effects due to the shared molecular mechanism or gene pathways. We will apply in-depth graphical modeling and analysis to identify the many-to-many relationships between phenome and genome to define disease subtypes.

Anticipated Findings

We expect from this project, with computational experiments and in-depth view of All of US phenome-genome data, to establish the robust estimation methods of disease multimorbidities and a set of novel analytical techniques to characterize complex disease dynamics. Meanwhile, provide novel, systems-level insight to the understanding of interconnected disease etiology, pleiotropic effects of disease genetics, epistasis, genetic-environmental interactions. We hope to provide novel insight for precision medicine such as early disease prevention, better optimized therapeutic strategies, disease subtypes and patient subgroups. We will validate and demonstrate our findings in case studies with clinical context. Other than the contribution to the body of the knowledge, we expect to offer a set of high-quality computed summary data characterizing phenome-genome relationships, with corresponding tools for user-friendly investigation by peer researchers.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Yaomin Xu - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Collaborators:

  • Yajing Li - Project Personnel, Vanderbilt University Medical Center
  • Brian Sharber - Project Personnel, Vanderbilt University Medical Center
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