TIGA: target illumination GWAS analytics

Abstract

Motivation 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. Methods 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. Results 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 Web application, datasets, and source code via: https://unmtid-shinyapps.net/tiga/. Supplementary information Supplementary data are available at Bioinformatics online.

DISEASES 2.0: a weekly updated database of disease–gene associations from text mining and data integration

“…TIGA [22] is a new weekly updated web resource that imports GWAS data from the NHGRI-EBI GWAS Catalog [17], maps SNPs to the nearest protein-coding genes, and evaluates the confidence of each gene-trait association. The latter is done by calculating an average rank score based on the number and distance of SNPs supporting the association, the p-value of the most significant SNP, and the number of studies supporting the association weighted by the Relative Citation Ratio [30] of the underlying publications.…”

“…However, GWAS results are complex to interpret; the identified SNPs are typically not the causal mutations, which due to linkage disequilibrium could reside anywhere within a chromosomal region that may contain multiple genes [20]. Several secondary GWAS databases, such as DistiLD [21] and TIGA [22], thus aim to help non-experts interpret GWAS results by integrating other relevant information and prioritizing the results.…”

“…This has jointly led to substantial improvements of the text-mining results. We have further updated DIS-EASES to import experimental data from a new GWAS resource, TIGA [22]. We map all disease-gene associations to a common set of identifiers and provide confidence scores for the associations, which are comparable across evidence types.…”

“…Together with technical improvements to the text-mining pipeline itself, this has led to a > 9-fold increase in the number of disease-gene associations extracted at any confidence cutoff. DISEASES has also been upgraded to use GWAS data via the new TIGA database [22], which increased the number of experimental associations by more than 70%. The database is freely available at https://diseases.jensenlab.org/ where it can be browsed via a web interface as well as downloaded in its entirety to facilitate large-scale analysis.…”

Getting Started with the IDG KMC Datasets and Tools

“…In a similar vein, Basic Protocol 11 discusses Harmonizome-ML, an Appyter that enables users to utilize the datasets from Harmonizome to build machine learning models that predict novel gene-attribute associations. Basic Protocol 12 includes a discussion of TIGA (Yang et al, 2021), Target Illumination GWAS Analytics, a tool that summarizes gene-trait associations derived from genome-wide association studies (GWAS) with rational and intuitive evidence metrics. In Basic Protocol 13, we describe how users can submit an input list of genes or differentially phosphorylated proteins to KEA3 for kinase enrichment analysis (Kuleshov et al, 2021) to infer kinases associated with the input list.…”