用细胞类型特异性知识图谱提升基因关联研究的可解释性
Incorporating contextual information into KGWAS for interpretable GWAS discovery
- 用疾病相关细胞类型的基因互作数据构建上下文感知的知识图谱
- 在不损失统计效能的前提下，大幅缩减原始知识图谱规模
- 基于扰动测序数据的稀疏图谱能发现更稳定可靠的致病网络
全基因组关联研究（GWAS）识别遗传变异与疾病之间的关联；然而，从关联走向因果机制对治疗靶点优先排序至关重要。最近提出的知识图谱全基因组关联研究（KGWAS）框架通过知识图谱（KG）将遗传变异与下游基因-基因互作相连接，从而提升检测能力并提供机制见解。但原始的KGWAS依赖于大型通用知识图谱，可能引入虚假相关。我们假设：来自疾病相关细胞类型的细胞类型特异性知识图谱更有利于疾病机制发现。本文表明，可在不损失下游任务统计效能的前提下显著剪枝原通用知识图谱，并通过整合扰动测序（perturb-seq）数据获得的基因-基因关系进一步提升性能。重要的是，使用直接来自扰动测序证据的稀疏、上下文特异性知识图谱，可得到更一致且生物学上更稳健的疾病关键网络。
原文摘要 · Abstract (English)
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.
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