Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Incorporating contextual information into KGWAS for interpretable GWAS discovery

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

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.

Incorporating contextual information into KGWAS for interpretable GWAS discovery

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.

Paper Structure

This paper contains 22 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Methods
  4. Knowledge Graph Formulation
  5. Heterogeneous Graph Attention Network
  6. Knowledge graph sparsification
  7. Disentangling G2G and G2P Contributions:
  8. Edge Type Selection:
  9. Context-aware Knowledge Graph Formulation
  10. Experimental Design
  11. Pre-processing of Perturb-Seq data
  12. Model Implementation and Training
  13. Evaluation Criteria
  14. Interpretability
  15. Results
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Knowledge graph construction. A. The original KG in KGWAS consisting of variant-gene, gene-gene and gene-program edges; B. Our extension to KGWAS: (left) removing gene-program edges, and sparsifying remaining connections; (right) replacing gene-gene edges with contextually relevant relationships derived from Perturb-seq.
  • Figure 2: Distribution of cosine similarities between pairs of target genes annotated in STRING (positive) and randomly selected pairs of target genes (negative).
  • Figure 3: Contributions of different nodes and edge types in the KGWAS knowledge graph using a sample size of 10,000. Reported metrics are the total number of recalled independent loci summed across three selected traits. Standard deviations are computed across three training runs with different random seeds. In each subplot, bold indicates the model selected as the baseline in the subsequent panel. (A) Ablation of different relation types; (B) Ablation of V2G and G2G edge types; (C) Collapsing V2G and G2G edge types; (D) Adding context-specific information.
  • Figure 4: Consistency of disease critical networks for the rs61759901 variant in KGWAS (left) and context-aware KGWAS (right). Each plot aggregate nodes and edges from three seeded models trained on the MCH trait. Genes that are not significant in the K562 cell line (n=6) are shown with a gray border.