Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal

Understanding how genetic variation shapes gene regulation in the developing human brain remains a central challenge in neuroscience, particularly in transient cell types and developmental stages where data are limited. We present Seal, an interpretable artificial intelligence (AI) transfer learning framework for genome-based modeling of gene expression and variant effects with spatiotemporal resolution across 26 regions, 30 cell types and 7 developmental stages. Seal predictions provide mechanistic interpretation by linking variants to transcriptional regulators in a spatiotemporal context. Applying Seal to genome-wide association studies, we identify shared and distinct regulatory architectures across six neuropsychiatric conditions that align with clinical trajectories. In the Simons Simplex Collection autism cohort whole-genome data, Seal uncovers a significant burden of de novo regulatory variants in transient fetal excitatory neurons, revealing cell-type-specific and stage-specific effects. By connecting genetic variation to spatiotemporal regulatory programs, Seal offers a general framework for uncovering mechanisms of human brain development and disease.