Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.

ABSTRACT: Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning-based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. We prioritized causal variants within disease- or trait-associated loci from all publicly available genome-wide association studies and experimentally validated predictions for four immune-related diseases. By exploiting the scalability of ExPecto, we characte

SUBMITTER: Zhou J 

PROVIDER: S-EPMC6094955 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature