Deep learning predicts the impact of regulatory variants on cell-type-specific enhancers in the brain.

ABSTRACT:

Motivation

Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization.

Results

Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of indiv

SUBMITTER: Zheng A 

PROVIDER: S-EPMC9887460 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature