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Leveraging genomic deep learning models for non-coding variant effect prediction

The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function remains an important challenge in human genetics. Genomic deep learning models have emerged as a promising approach to enable in silico prediction of variant effects. These include supervised sequence-to-activity models, which predict genome-wide chromatin states or gene expression levels directly from DNA sequence, and self-supervised genomic language models. Here, we review progress in leveraging these models for non-coding variant effect prediction. We describe practical considerations for making such predictions and categorize the types of ground truth data that have been used to evaluate deep learning-based variant effect predictions, providing insight into the settings in which current models are most useful. We also discuss downstream applications of such models to understanding disease-relevant non-coding variants. Our review highlights key considerations for practitioners and opportunities for future improvements in model development and evaluation.

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