| Published Version Download ( PDF | 1MB) | License: Creative Commons Attribution 4.0 |
KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies
Stanzick, Kira J.
, Stark, Klaus J., Gorski, Mathias
, Schödel, Johannes, Krüger, René, Kronenberg, Florian, Warth, Richard
, Heid, Iris M. and Winkler, Thomas W.
(2023)
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 (1).
Date of publication of this fulltext: 05 Dec 2023 10:10
Article
DOI to cite this document: 10.5283/epub.55162
Abstract
BackgroundGenome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease ...
BackgroundGenome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise.MainHere we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects ("SNP-to-gene" mapping), (ii) genes with kidney phenotypes in mice or human ("gene-to-phenotype"), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function ("GPS tab") to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data (https://kidneygps.ur.de/gps/).ConclusionWith the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.
Alternative links to fulltext
Involved Institutions
Details
| Item type | Article | ||||
| Journal or Publication Title | BMC Bioinformatics | ||||
| Publisher: | BMC | ||||
|---|---|---|---|---|---|
| Open Access Type: | DEAL (Springer Gold) | ||||
| Place of Publication: | LONDON | ||||
| Volume: | 24 | ||||
| Number of Issue or Book Chapter: | 1 | ||||
| Date | 21 September 2023 | ||||
| Institutions | Medicine > Institut für Epidemiologie und Präventivmedizin > Lehrstuhl für Genetische Epidemiologie Biology, Preclinical Medicine > Institut für Physiologie > Prof. Dr. Richard Warth | ||||
| Identification Number |
| ||||
| Keywords | ; GWAS; Kidney function; Web application; KidneyGPS; Gene prioritization | ||||
| Dewey Decimal Classification | 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Yes | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-551620 | ||||
| Item ID | 55162 |
Download Statistics
Download Statistics