Improved detection of genetic effects on promoter usage with augmented transcript annotations

Abstract Disease-associated non-coding variants can modulate their target genes by disrupting multiple mechanisms, including regulating total gene expression level, splicing, alternative polyadenylation or promoter usage. Quantifying promoter usage from standard RNA sequencing data is challenging due to incomplete reference transcriptome annotations and low read coverage observed at the ends of transcripts. We previously developed the txrevise tool ( https://github.com/kauralasoo/txrevise ) to quantify promoter usage events from RNA-seq data using reference transcriptome annotations. Here, we augment the txrevise promoter event annotations with experimentally identified Cap Analysis of Gene Expression (CAGE) promoters from the FANTOM5 project. Applying the new annotations to RNA-seq data from 358 individuals, we found that augmented promoter event annotations increased the power to detect promoter usage quantitative trait loci (puQTLs) by ~30%. However, concordance between puQTLs inferred from RNA-seq data and those directly measured using CAGE remained low, suggesting that additional experimental and computational improvements are needed to capture the full range of regulatory effects of non-coding variants.