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Updated: Mar 10, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
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PCAN: phenotype consensus analysis to support disease-gene association.

Patrice Godard1, Matthew Page2

  • 1Clarivate Analytics (formerly the IP & Science business of Thomson Reuters), 5901 Priestly Dr., #200, Carlsbad, CA, 92008, USA.

BMC Bioinformatics
|December 8, 2016
PubMed
Summary

Phenotype Consensus Analysis (PCAN) identifies gene-phenotype links by analyzing gene signaling networks. This method reveals significant phenotype consensus in ~67% of disease-gene associations, aiding target discovery and therapy development.

Keywords:
Biological networksDisease-gene associationGeneticsPhenotypeSemantic similarity

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Area of Science:

  • Genomics and Bioinformatics
  • Systems Biology
  • Translational Medicine

Background:

  • Bridging genotype and phenotype is crucial for targeted therapies and drug discovery.
  • Integrating gene-phenotype data within molecular networks is vital for prioritizing disease-related genes.

Purpose of the Study:

  • To introduce Phenotype Consensus Analysis (PCAN) for assessing phenotype similarity in gene signaling neighborhoods.
  • To demonstrate PCAN's utility in prioritizing and interpreting genes associated with diseases.

Main Methods:

  • Phenotype Consensus Analysis (PCAN) assesses semantic similarity of phenotypes within a candidate gene's signaling network.
  • Utilized high-quality interaction data from STRING and pathway information from Metabase.
  • Applied PCAN to ~4,549 OMIM disease-gene associations.

Main Results:

  • Significant phenotype consensus (p < 0.05) was observed in approximately 67% of analyzed disease-gene associations.
  • Demonstrated PCAN's ability to highlight discriminatory traits for mechanistically related genes using Joubert Syndrome as a case study.
  • Identified significant phenotype consensus across a large set of OMIM disease-gene associations.

Conclusions:

  • Phenotype consensus is an intuitive and versatile method for disease-gene association studies.
  • PCAN facilitates the mechanistic understanding of diverse phenotypes.
  • PCAN is available as an R package for community use, aiding gene prioritization workflows.