G2D

G2D prioritizes candidate genes within chromosomal regions to identify genes potentially associated with inherited diseases.

Key Features:

• Gene Prioritization Algorithms: Three distinct algorithms prioritize genes using disease phenotype (OMIM identifier), known or suspected associated genes (Entrez Gene identifiers), or a second genomic region with gene interactions extracted from STRING.
• Comprehensive Data Integration: Integrates data mining of biomedical databases with sequence analysis to evaluate both annotated and unannotated genes within specified chromosomal regions.
• Precomputed Analyses: Includes precomputed analyses for over 600 genetically inherited diseases mapped to chromosomal regions without an assigned gene, with specific examples such as asthma.
• Scoring System: Implements a scoring system that assesses potential functional relationships between genes and diseases and showed significant correlation with disease association in benchmark tests.
• Sequence Homology Retrieval: Retrieves candidate genes through sequence homology searches to detect related sequences.

Scientific Applications:

• Linkage and Association Studies: Assists interpretation of regions from genome-wide linkage and association studies by prioritizing candidate genes within mapped intervals.
• Candidate Set Reduction: Narrows large experimental candidate gene sets to the most promising genes for follow-up analysis.
• Evaluation of Annotated and Predicted Genes: Supports analysis of both well-characterized (annotated) and predicted (unannotated) genes within disease-linked regions.
• Application to Complex Traits: Has been applied to complex traits such as type 2 diabetes (T2D) and obesity, identifying candidate genes including LPL and BCKDHA.

Methodology:

Performs data mining on biomedical databases and gene sequence analysis, retrieves genes via sequence homology searches, extracts gene interactions from STRING, and scores candidates according to the selected prioritization algorithm.