Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

ABSTRACT: Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet, the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here, we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequenc

SUBMITTER: Hoffman GE 

PROVIDER: S-EPMC6847046 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature