Single-cell gene expression prediction from DNA sequence at large contexts

Human genetic variants impacting traits such as disease susceptibility frequently act through modulation of gene expression in a highly cell-type-specific manner. Computational models capable of predicting gene expression directly from DNA sequence can assist in the interpretation of expression-modulating variants, and machine learning models now operate at the large sequence contexts required for capturing long-range human transcriptional regulation. However, existing predictors have focused on bulk transcriptional measurements where gene expression heterogeneity can be drowned out in broadly defined cell types. Here, we use a transfer learning framework, seq2cells, leveraging a pre-trained epigenome model for gene expression prediction from large sequence contexts at single-cell resolution. We show that seq2cells captures cell-specific gene expression beyond the resolution of pseudo-bulked data. Using seq2cells for variant effect prediction reveals heterogeneity within annotated cell types and enables in silico transfer of variant effects between cell populations. We demonstrate the challenges and value of gene expression and variant effect prediction at single-cell resolution, and offer a path to the interpretation of genomic variation at uncompromising resolution and scale.