Abstract
Genome-wide association studies (GWAS) identify numerous disease-linked genetic variants at noncoding genomic loci, yet therapeutic progress is hampered by the challenge of deciphering the regulatory roles of these loci in tissue-specific contexts. Single-cell multimodal assays that simultaneously profile chromatin accessibility and gene expression could predict tissue-specific causal links between noncoding loci and the genes they affect. However, current computational strategies either neglect the causal relationship between chromatin accessibility and transcription or lack variant-level precision, aggregating data across genomic ranges due to data sparsity. To address this, we introduce GrID-Net, a graph neural network approach that generalizes Granger causal inference to detect new causal locus-gene associations in graph-structured systems such as single-cell trajectories. Inspired by the principles of optical parallax, which reveals object depth from static snapshots, we hypothesize that causal mechanisms could be inferred from static single-cell snapshots by exploiting the time lag between epigenetic and transcriptional cell states, a concept we term "cell-state parallax." Applying GrID-Net to schizophrenia (SCZ) genetic variants, we increase variant coverage by 36% and uncovered noncoding mechanisms that dysregulate 132 genes, including key potassium transporters such as KCNG2 and SLC12A6. Furthermore, we discover evidence for the prominent role of neural transcription-factor binding disruptions in SCZ etiology. Our work not only provides a strategy for elucidating the tissue-specific impact of noncoding variants but also underscores the breakthrough potential of cell-state parallax in single-cell multiomics for discovering tissue-specific gene regulatory mechanisms.