Back

Bridging the gap between genome-wide association studies and network medicine with GNExT

Arend, L.; Woller, F.; Rehor, B.; Emmert, D.; Frasnelli, J.; Fuchsberger, C.; Blumenthal, D. B.; List, M.

2026-02-02 bioinformatics
10.64898/2026.01.30.702559 bioRxiv
Show abstract

MotivationA growing volume of large-scale genome-wide association study (GWAS) datasets offers unprecedented power to uncover the genetic determinants of complex traits, but existing web-based platforms for GWAS data exploration provide limited support for interpreting these findings within broader biological systems. Systems medicine is particularly well-suited to fill this gap, as its network-oriented view of molecular interactions enables the integration of genetic signals into coherent network modules, thereby opening opportunities for disease mechanism mining and drug repurposing. ResultsWe introduce GNExT (GWAS Network Exploration Tool), a web-based platform that significantly extends the scope of exploration of variant-level effects and significance beyond those provided by existing solutions. By including MAGMA and Drugst.One, GNExT allows its users to study genetic variants in the context of the latest systems medicine approaches, extending to the identification of potential drug repurposing candidates. Moreover, GNExT advances platform implementation well beyond the current state of the art by offering a highly standardized Nextflow pipeline for data import and preprocessing, allowing researchers to deploy their study results on a sophisticated web interface with minimal implementation overhead. We demonstrate the utility of GNExT using a genome-wide association meta-analysis of human olfactory identification, in which the framework translated isolated GWAS signals to potential pharmacological targets in human olfaction. Furthermore, the deployment of a GNExT instance on European-ancestry Pan-UK Biobank data demonstrates the frameworks scalability, resulting in a comprehensive large-scale resource encompassing thousands of traits and enabling new network medicine-based investigations. Availability and ImplementationThe complete GNExT ecosystem, including the Nextflow preprocessing pipeline, the backend service, and frontend interface, is publicly available on GitHub (https://github.com/dyhealthnet/gnext_nf_pipeline, https://github.com/dyhealthnet/gnext_platform). The public instances of the GNExT platform on olfaction and Pan-UKBB are available under https://olfaction.gnext.gm.eurac.edu and https://panukbb-eur.gnext.gm.eurac.edu.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
1061 papers in training set
Top 1%
22.2%
2
Nature Communications
4913 papers in training set
Top 19%
10.0%
3
Patterns
70 papers in training set
Top 0.1%
8.3%
4
Bioinformatics Advances
184 papers in training set
Top 0.5%
6.3%
5
Genome Medicine
154 papers in training set
Top 1%
4.8%
50% of probability mass above
6
Advanced Science
249 papers in training set
Top 6%
3.5%
7
npj Digital Medicine
97 papers in training set
Top 1%
3.5%
8
Computational and Structural Biotechnology Journal
216 papers in training set
Top 3%
2.4%
9
Cell Genomics
162 papers in training set
Top 3%
2.1%
10
Nature Methods
336 papers in training set
Top 4%
2.1%
11
Briefings in Bioinformatics
326 papers in training set
Top 4%
1.7%
12
BMC Medical Genomics
36 papers in training set
Top 0.5%
1.7%
13
Journal of the American Medical Informatics Association
61 papers in training set
Top 1%
1.3%
14
Molecular Systems Biology
142 papers in training set
Top 0.9%
1.3%
15
npj Systems Biology and Applications
99 papers in training set
Top 1%
1.3%
16
Scientific Reports
3102 papers in training set
Top 64%
1.3%
17
Nucleic Acids Research
1128 papers in training set
Top 14%
1.2%
18
Nature Machine Intelligence
61 papers in training set
Top 3%
1.2%
19
NAR Genomics and Bioinformatics
214 papers in training set
Top 3%
1.1%
20
Nature Genetics
240 papers in training set
Top 6%
1.1%
21
GigaScience
172 papers in training set
Top 2%
1.1%
22
BMC Bioinformatics
383 papers in training set
Top 6%
0.9%
23
Cell Systems
167 papers in training set
Top 11%
0.9%
24
Genome Biology
555 papers in training set
Top 6%
0.9%
25
Nature
575 papers in training set
Top 15%
0.8%
26
PLOS ONE
4510 papers in training set
Top 66%
0.8%
27
The American Journal of Human Genetics
206 papers in training set
Top 4%
0.7%
28
Journal of Biomedical Informatics
45 papers in training set
Top 1%
0.7%
29
iScience
1063 papers in training set
Top 33%
0.7%
30
Nature Medicine
117 papers in training set
Top 5%
0.7%