Complex trait heritability is systematically organized across intercellular communication interfaces in addition to cell-intrinsic programs, with genetic risk concentrating in tissue- and trait-specific ligand–receptor channels that are statistically independent from cell-autonomous signals, replicate across independent datasets and platforms, and identify therapeutically relevant genes largely absent from standard prioritization methods.

Methods that map genetic risk to cells identify disease-relevant tissues and cell types but cannot test whether genetic effects concentrate at molecular interfaces between cells. Here we introduce EdgeMap, which integrates spatial transcriptomics with GWAS summary statistics to partition trait heritability into cell-intrinsic and intercellular components and to resolve the intercellular signal into specific ligand–receptor channels. Across 17 traits and five human tissues, edge heritability is enriched in biologically coherent trait–tissue pairings (3.8-fold; P = 4.4 × 10−6) and replicates across independent tissue sections, GWAS cohorts, and cell-segmented Visium HD. Per-pair decomposition identifies 67 trait-specific channels (FDR < 0.10) organized into convergent pathway families—neurexin–neuroligin synaptic signaling in bipolar disorder, vascular adhesion in cardiovascular traits, and lipoprotein-clearance pathways in liver. Most edge genes are absent from standard gene-level prioritization, supporting intercellular communication as a complementary dimension of genetic architecture.