TIGA

TIGA prioritizes gene-trait associations from genome-wide association studies (GWAS) to support drug target ranking by aggregating multistudy evidence and deriving confidence scores that link protein-coding genes to phenotypes.

Key Features:

• Rational Ranking and Filtering: Provides systematic evaluation of the strength, specificity, and relevance of genotype-phenotype associations derived from GWAS data for target prioritization.
• Data Aggregation Across Studies: Aggregates GWAS data across multiple studies using existing curation and harmonization efforts to enable cross-study assessment of associations.
• Confidence Scoring System: Computes confidence scores for gene-trait associations using aggregated statistics and bibliometric metrics, including iCite Relative Citation Ratio and meanRank scores, to link protein-coding genes with phenotypes.
• Scientific Consensus Evaluation: Incorporates bibliometric assessment to quantify the level of scientific consensus around specific gene-trait associations.

Scientific Applications:

• Drug target hypothesis generation and prioritization: Produces ranked and scored gene-trait associations to support identification and ranking of candidate therapeutic targets from GWAS evidence.
• Cross-study interpretation of GWAS evidence: Enables assessment of association robustness and relevance by integrating evidence across multiple GWAS datasets.

Methodology:

Aggregates multivariate evidence from multiple GWAS studies, performs statistical analysis and bibliometric evaluations (including iCite Relative Citation Ratio and meanRank scores), and derives aggregated confidence scores linking protein-coding genes to phenotypes using curated and harmonized datasets.