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GPS - an approach to GenePrioritiSation

Selection of GWAS-supported drug targets has been shown to improve the success rate in drug discovery, especially if there is a high confidence link between a GWAS SNP and the gene (King et al. 2019). Therefore, genes under GWAS loci associated with kidney function biomarkers are interesting targets to modulate kidney function. In particular, loci associated with kidney function decline might be interesting targets to counteract the progressive loss of kidney function. In order to identify the most likely causal genes and genetic variants from the large number of genes and genetic variants in GWAS loci, we are working on a systematic gene prioritisation. To do so, we are looking at different methods for the finemapping of the statistical association signal and the integration of functional data. Our current gene prioritisation approach is based on 424 loci and 594 independent association signals with creatinine-based estimated glomerular filtration rate (eGFR) as a marker for kidney function (Stanzick et al. BMC Bioinformatics 2023Stanzick et al. Nat Commun 2021).

KidneyGPS - a web-based software tool to support GPS for kidney function loci

The results of gene prioritisation based on GWAS meta-analyses (GWAMA) and post-GWAMA are high-dimensional and diverse. To provide easier access to the relevant results from GWAMAs and post-GWAMAs for kidney function and kidney function decline to experts from other fields (e.g. medicine, physiology, biology), we have developed KidneyGPS as a user-friendly web-application. KidneyGPS enables easy access by search functions on genes, variants, and regions, to prioritize genes and variants likely relevant for kidney function in humans for functional follow-up. Several options allow customizing the presented output according to the specific needs of the user.

Click here to access our Gene PrioritiSation tool: 


If you use this tool, please cite: 

  • Stanzick, K.J., ... Heid I.M., Winkler T.W. KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies. BMC Bioinformatics 24, 355 (2023). https://doi.org/10.1186/s12859-023-05472-0 

This work is conducted within the SFB-1350 / TRR 374.

  1. Fakultät für Medizin