Columbia Technology Ventures

Database of annotated hair follicle genes for the identification of genetic links to disease

This technology is a database of annotated genes involved in hair follicle biology that enables the identification of key gene-phenotype relationships in hair disorders.

Unmet Need: Efficient method to identify gene-disease links in hair disorders

Advances in genome sequencing have enabled large databases that detail gene-disease relationships but fail to fully integrate information preventing us from understanding how genes act together participating in pathways and how pathways interact with each other to maintain tissue homeostasis and prevent disease. Such knowledge would allow us to identify and prioritize biological points of intervention for therapeutic development and may reveal rationale for drug repurposing. For dermatological diseases such as hair disorders, hundreds of genes have been identified for which a single mutation is sufficient to generate disease. However, these genes have yet to be subject to comprehensive analysis. As such, there is a need for a method that assembles and annotates genes associated with hair disorders to identify key gene-phenotype relationships for improved diagnosis and treatment.

The Technology: Database of annotated hair follicle genes organized into gene clusters for improved assignment of gene-disease links

This technology is a database of annotated genes involved in hair follicle biology that enables the identification of relationships among genetic drivers of disease. Using an unsupervised hierarchical algorithm, a database of nearly 700 genes with single mutations associated with observable disease phenotypes were organized into 35 gene clusters. This strategy also uses an annotation enrichment analysis to provide high-resolution characterization of relevant physiological processes. By providing a large database of genes involved in hair disorders and clustering them into biological modules, this technology may be used to identify key gene-phenotype relationships for improved diagnosis and treatment of hair disorders.

This technology was used to identify previously unknown genetic contributions from Hippo signaling to hair disorders.

Applications:

  • Identification of potential therapeutic targets and biomarkers for hair disorders
  • Identification of biomarkers for monitoring treatment efficacy and patient outcomes
  • Analysis of gene-disease interactions
  • Data mining of genetic database for genetic and phenotypic disease associations
  • Precision medicine

Advantages:

  • Algorithm automatically determines gene clusters in a dataset
  • Machine learning approach
  • Can be applied to find gene-disease links in a range of disorders

Lead Inventor:

Lynn Petukhova, Ph.D.

Related Publications:

Tech Ventures Reference: