Incorporating contextual information into KGWAS for interpretable GWAS discovery

Cheng Jiang, Brady Ryan, Megan Crow, Kipper Fletez-Brant, Kashish Doshi, Sandra Melo, Kexin Huang, Burkhard Hoeckendorf, Heming Yao, David Richmond

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Track: long paper (4–8 pages excluding references)

Keywords: GWAS, Graph Attention Network, Perturb-seq Screens, Knowledge Graph

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.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 39