TIGA: target illumination GWAS analytics

Abstract

Motivation: Genome-wide association studies 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.

Results: 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.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

Availability and implementation: Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

GWAS Catalog study counts by year and vendor, indicating growth and platform trends

Fig. 2.

GWAS Catalog sample size distributions by year, on log scale, indicating variance in statistical power

Fig. 3.

TIGA web application. The image displays 644 genes currently associated with trait ‘high-density lipoprotein cholesterol measurement’ (EFO_0004612)

Fig. 4.

Performance evaluation. The performance of TIGA on the gold standard of gene–disease associations. (A) Results for the top 3 individual variables of merit. (B) Results for the multivariate ranking by meanRankScore and μ score

Fig. 5.

Examples of understudied (Tbio) genes for trait ‘high-density lipoprotein cholesterol measurement’ in TIGA

Fig. 6.

Provenance for association between gene GIMAP6 ‘GTPase IMAP family member 6’ and trait ‘high-density lipoprotein cholesterol measurement’

Fig. 7.

TIGA web application, displaying a plot of genes associated with trait ‘HbA1c measurement’ (EFO_0004541)

Fig. 8.

Provenance for association between gene SLC25A44 ‘Solute carrier family 25 member 44’ and trait ‘HbA1c measurement’

Fig. 9.

TIGA data sources and interfaces. TIGA integrates GWAS data from the Catalog and several other sources to rank gene–disease associations. These associations can be accessed through the TIGA webapp and are integrated into the DISEASES (Pletscher-Frankild et al., 2015) and Pharos platforms. Bulk download is also available