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.

Workflow

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Bibtex

@inproceedings{
  jiang2026incorporating,
  title={Incorporating contextual information into {KGWAS} for interpretable {GWAS} discovery},
  author={Cheng Jiang and Brady Ryan and Megan Crow and Kipper Fletez-Brant and Kashish Doshi and Sandra Melo and Kexin Huang and Burkhard Hoeckendorf and Heming Yao and David Richmond},
  booktitle={Learning Meaningful Representations of Life (LMRL) Workshop at ICLR 2026},
  year={2026},
}