Incorporating contextual information into KGWAS for interpretable GWAS discovery

2026-03-26

AI Summary

Traditional genome-wide association studies (GWAS) struggle to uncover causal disease mechanisms, while existing knowledge graph–enhanced GWAS (KGWAS) approaches rely on generic knowledge graphs that often introduce spurious associations. To address this limitation, this work proposes a novel KGWAS framework that integrates perturb-seq–derived, cell-type-specific gene interaction networks to construct context-specific knowledge graphs, replacing generic ones. By combining knowledge graph pruning, data-driven modeling of gene relationships, and rigorous statistical inference, the method significantly enhances the sparsity, biological coherence, and robustness of identified disease pathways—without compromising statistical power.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• GWAS
• causal mechanisms
• knowledge graph
• cell-type specificity
• disease mechanism

Innovation

• context-specific knowledge graph
• KGWAS
• perturb-seq
• GWAS interpretation
• causal mechanism discovery