Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics

Abstract

Transcriptome data is commonly used to understand genome function via quantitative trait loci (QTL) mapping and to identify the molecular mechanisms driving genome wide association study (GWAS) signals through colocalization analysis and transcriptome-wide association studies (TWAS). While RNA sequencing (RNA-seq) has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, such studies are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry (Pan-transcriptomic phenotyping), a framework to efficiently generate diverse RNA phenotypes from RNA-seq data and perform downstream integrative analyses with genetic data. Pantry currently generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We applied Pantry to Geuvadis and GTEx data, and found that 4,768 of the genes with no identified expression QTL in Geuvadis had QTLs in at least one other transcriptional modality, resulting in a 66% increase in genes over expression QTL mapping. We further found that QTLs exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities (xTWAS) approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs. We provide the Pantry code, RNA phenotypes from all Geuvadis and GTEx samples, and xQTL and xTWAS results on the web.