Target Illumination GWAS Analytics
Aggregating and assessing experimental evidence for interpretable, explainable, accountable gene-trait associations.
Goal: Interpretable, useful knowledge from complex and noisy GWAS data
Genome wide association studies (GWAS) can reveal important genotype–phenotype associations, however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. Here, we describe rational ranking, filtering and interpretation of inferred gene–trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene–trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene–trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite Relative Citation Ratio, and meanRank scores, to aggregate multivariate evidence. TIGA is intended for drug target hypothesis generation, scoring and ranking, via the TIGA web app, and integration by IDG TCRD+Pharos, and the JensenLab DISEASES resource.