A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs.

ABSTRACT: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants. Through the application to several experimental datasets, we demonstrate that the proposed method significantly improves prediction accuracy compared to existing functional prediction methods at the tissue/cell type level, but especially so at the organism level. Importantly, we illustrate how the GenoNet scores can help in fine-mapping at GWAS loci, and in the discovery of di

SUBMITTER: He Z 

PROVIDER: S-EPMC6281617 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature