Predicting cell-type-specific non-coding RNA transcription from genome sequence

Abstract

Transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). However, because transcription of ncRNAs, especially enhancer RNAs, is often low and cell type-specific, its dependency on genotype remains largely unexplored. Here, we developed mutation effect prediction on ncRNA transcription (MENTR), a quantitative machine learning framework reliably connecting genetic associations with expression of ncRNAs, resolved to the level of cell type. MENTR-predicted mutation effects on ncRNA transcription were concordant with estimates from previous genetic studies in a cell type-dependent manner. We inferred reliable causal variants from 41,223 GWAS variants, and proposed 7,775 enhancers and 3,548 long-ncRNAs as complex trait-associated ncRNAs in 348 major human primary cells and tissues, including plausible enhancer-mediated functional alterations in single-variant resolution in Crohns disease. In summary, we present new resources for discovering causal variants, the biological mechanisms driving complex traits, and the sequence-dependency of ncRNA regulation in relevant cell types.