将上下文信息融入KGWAS以实现可解释的GWAS发现

Genome-Wide Association Studies (GWAS) identify associations between genetic variants and diseases, but moving from associations to causal mechanisms is essential for prioritizing therapeutic targets. 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 enhancing detection power and providing mechanistic insights. However, the original KGWAS implementation relies on a large general-purpose KG, which can introduce spurious correlations. It is hypothesized that cell-type specific KGs from disease-relevant cell types will better support disease mechanism discovery. Results demonstrate that the effectiveness of the general-purpose KG in KGWAS can be significantly improved by using cell-type specific KGs, thereby enhancing the understanding of disease mechanisms.