PheMIME & Phe-OmicsMIME: Interactive Visualizations for Phenome, Multi-Omics Multimorbidity Analysis

Abstract Number:

2092 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Siwei Zhang (1), Nick Strayer (2), Tess Vessels (3), Dan Roden (4), Douglas Ruderfer (3), Yaomin Xu (1)

Institutions:

(1) Vanderbilt University Medical Center, Nashville, TN, (2) Posit PBC, Boston, MA, (3) Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, (4) Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN

Co-Author(s):

Nick Strayer  
Posit PBC
Tess Vessels  
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center
Dan Roden  
Department of Pharmacology, Vanderbilt University Medical Center
Douglas Ruderfer  
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center
Yaomin Xu  
Vanderbilt University Medical Center

First Author:

Siwei Zhang  
Vanderbilt University Medical Center

Presenting Author:

Siwei Zhang  
Vanderbilt University Medical Center

Abstract Text:

To address the need for novel interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed PheMIME and Phe-OmicsMIME. Integrating data from three large-scale EHR systems and genetic Biobanks: VUMC, MGB and UK Biobank, these tools perform statistical network analysis for efficient visualization and disease subtype analysis, uncovering robust and novel disease links that are interoperable across different systems to aid personalized medicine. PheMIME integrates phenome-wide analyses(PheWAS) summary statistics and incorporates an enhanced version of associationSubgraphs, enabling dynamic inference of disease clusters. Phe-OmicsMIME predict multi-omics traits from genetic Biobank and integrates hazard ratios from time-to-event PheWAS, connecting gene-protein-metabolite-disease relationships. It facilitates exploration of the biomolecule-disease bipartite network and provides compelling evidence of shared pathways among diseases. These tools stand out as the first of their kind to offer extensive disease subtype knowledge integration with substantial support for efficient online analysis and interactive visualization.

Keywords:

interactive visualization|UK Biobank and Electronic Health Records (EHR)|network analysis and data science|interoperability and reproducibility|multimorbidity and PheWAS|personalized medicine

Sponsors:

Biometrics Section

Tracks:

Miscellaneous

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