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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Improved human disease candidate gene prioritization using mouse phenotype.

Jing Chen¹, Huan Xu, Bruce J Aronow

¹Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. Jing.Chen@cchmc.org

BMC Bioinformatics

|October 18, 2007

Summary

This summary is machine-generated.

Mouse phenotype data significantly enhances human disease gene prioritization by identifying causal genes. This approach, using ToppGene, outperforms existing methods for complex genetic diseases.

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Complex diseases arise from multifactorial genetic and environmental influences.
Genome-wide studies identify gene associations but struggle with prioritizing specific causal genes.
Existing methods for candidate gene prioritization have limitations.

Purpose of the Study:

To evaluate the utility of mouse phenotype data for human disease gene prioritization.
To introduce and validate an integrated data approach for identifying disease-associated genes.
To compare the performance of the novel approach against existing prioritization tools.

Main Methods:

Integration of mouse phenotype data with human gene information.
Development and application of the ToppGene computational tool.
Comparative analysis with SUSPECTS and ENDEAVOUR candidate gene prioritization methods.

Main Results:

Demonstrated the significant utility of mouse phenotype data in human disease gene prioritization.
Showcased that the ToppGene approach outperforms SUSPECTS and ENDEAVOUR.
Validated the effectiveness of integrating diverse biological data for gene discovery.

Conclusions:

Incorporating mouse ortholog phenotype information substantially improves human disease gene analysis.
The ToppGene tool offers a superior method for prioritizing candidate genes in complex diseases.
This strategy advances the identification of genes underlying human genetic disorders.