Incorporating contextual information into KGWAS for interpretable GWAS discovery

TL;DR

This paper enhances the KGWAS framework by incorporating cell-type specific and perturb-seq derived gene relationships, improving disease mechanism discovery and network robustness.

Contribution

It demonstrates that pruning the general KG and adding context-specific data improves GWAS interpretability and biological relevance.

Findings

Pruning the general KG does not reduce statistical power.

Incorporating perturb-seq data enhances network robustness.

Context-specific KGs yield more biologically consistent disease networks.

Abstract

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.