Incorporating contextual information into KGWAS for interpretable GWAS discovery
This work addresses the challenge of interpretable GWAS discovery for disease mechanism identification, though it is incremental as it builds on the existing KGWAS framework.
The study tackled the problem of spurious correlations in Knowledge Graph GWAS (KGWAS) by showing that pruning general-purpose knowledge graphs and incorporating cell-type specific data from perturb-seq improves performance, yielding more consistent and biologically robust disease-critical networks without loss of statistical power.
Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge Graph GWAS (KGWAS) framework addresses this challenge by linking genetic variants to downstream gene-gene interactions via a knowledge graph (KG), thereby improving detection power and providing mechanistic insights. However, the original KGWAS implementation relies on a large general-purpose KG, which can introduce spurious correlations. We hypothesize that cell-type specific KGs from disease-relevant cell types will better support disease mechanism discovery. Here, we show that the general-purpose KG in KGWAS can be substantially pruned with no loss of statistical power on downstream tasks, and that performance further improves by incorporating gene-gene relationships derived from perturb-seq data. Importantly, using a sparse, context-specific KG from direct perturb-seq evidence yields more consistent and biologically robust disease-critical networks.