A multi-trait evaluation of network propagation for GWAS results

High dimensional genetic data is widely used to explore simple relations between traits, diseases and relevant genetic factors with high effect size, although identifying factors with small effect sizes or synergistic effects needs sophisticated solutions. A promising option is the application of network propagation methods for amplifying genome-wide association (GWA) results by incorporating a large amount of biological knowledge. This approach is also supported by the increasing availability of GWA summary statistics for comprehensive sets of phenotypes, frequently from multiple biobanks. However, the application of network propagation methods in GWA context is still in its early phase, despite its established role in the analysis of gene expression data and rare variants. First, we introduce the complete network-based GWAS workflow, also extending it for the simultaneous analysis of multiple traits and diseases. Second, we overview critical steps, possible solutions, and publicly available resources for this workflow; namely (1) the reliability of GWA results, (2) gene definitions and aggregation methods, (3) context-specific molecular networks, and (4) network propagation methods. Third, we present results from our large-scale evaluation of these options, such as established gene-disease relations, reference pathways, and recent public GWA results for hundreds of phenotypes from the UK Biobank. Results show serious inconsistencies in all settings regarding tested phenotypes, input transformations, networks, and network propagation method. This suggests a central unresolved issue in the application of this methodology for amplifying GWA results, and we hypothesize that the currently applied molecular networks form a serious bottleneck for the much expected multi-trait analysis.