Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.

ABSTRACT: Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap,

SUBMITTER: Lai B 

PROVIDER: S-EPMC9135341 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature