HGCS

HGCS prioritizes disease-causing gene variants by leveraging biological proximity to known phenotype-associated core genes using human gene-specific connectomes for analysis of high-throughput genomic datasets.

Key Features:

• Biological proximity-based prioritization: Prioritizes candidate genes based on functional proximity between genes, under the hypothesis that causal genes related to a phenotype are functionally close to each other.
• Core-gene anchoring: Uses core genes already known to be associated with the phenotype as anchors for prioritizing candidate genes.
• High-throughput dataset compatibility: Operates on variant lists derived from high-throughput genomic technologies to narrow down candidate disease-causing genes.
• Human gene-specific connectomes: Generates and updates human gene-specific connectomes that map biological relationships among genes for use in prioritization.
• Identification and ranking of candidates: Identifies and ranks putative disease-causing genes from variant-rich datasets based on computed biological proximity metrics.

Scientific Applications:

• Prioritization of candidate genes: Narrowing candidate gene lists from high-throughput genomic datasets for downstream experimental validation.
• Disease-causing variant identification: Identifying and ranking putative causative gene variants associated with human disease phenotypes.
• Novel candidate discovery: Pinpointing novel candidate genes linked to diseases by leveraging proximity to known phenotype-associated genes.
• Gene relationship mapping: Constructing and expanding gene-specific connectomes to map biological relationships among genes in human genetics research.

Methodology:

Computes biological proximity between genes, anchors prioritization on phenotype-associated core genes, and generates updated and extended human gene-specific connectomes.