Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits

Functional impact of noncoding variants can be predicted using computational approaches. Although predictive scores can be insightful, implementing the scores for a custom variant set and associating scores with complex traits require multiple phases of analysis. Here, we present a protocol for prioritizing variants by generating deep-learning-predicted functional scores and relating them with brain traits. We describe steps for score prediction, statistical comparison, phenotype correlation, and functional enrichment analysis. This protocol can be generalized to different models and phenotypes. For complete details on the use and execution of this protocol, please refer to Mondragon-Estrada et al.¹.