Leveraging genomic deep learning models for the prediction of non-coding variant effects

TL;DR

This review discusses how genomic deep learning models, including supervised and self-supervised approaches, are used to predict the effects of non-coding genetic variants, highlighting current progress, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of the state-of-the-art in leveraging deep learning models for non-coding variant effect prediction, including practical considerations and evaluation strategies.

Findings

Deep learning models can predict molecular phenotypes from DNA sequences.

Evaluation of models depends on ground truth data types.

Current models are most useful in specific genomic contexts.

Abstract

Characterizing non-coding variant 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 molecular phenotypes such as 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 used to evaluate variant effect predictions, providing insight into the settings in which current models are most useful. Our Review highlights key considerations for practitioners and opportunities for improvement in model development and evaluation.