A general framework for predicting the transcriptomic consequences of non-coding variation and small molecules.

ABSTRACT: Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regulari

SUBMITTER: Abdalla M 

PROVIDER: S-EPMC9041867 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature