Using population-scale transcriptomic and genomic data to map 3' UTR alternative polyadenylation quantitative trait loci

3' UTR alternative polyadenylation (APA) quantitative trait loci (3'aQTL) can explain approximately 16.1% of trait-associated non-coding variants and is largely distinct from other molecular QTLs. Here, we describe a bioinformatic protocol for identifying 3'aQTLs through standard RNA-seq and matched genomic data. This protocol allows users to analyze dynamic APA events, identify common genetic variants associated with differential 3' UTR usage, and predict the potential causal variants that affect APA.For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

Automated summary

This article describes a bioinformatic protocol for identifying 3'aQTLs using RNA-seq and genomic data, allowing analysis of dynamic APA events, identification of genetic variants associated with differential 3' UTR usage, and prediction of potential causal variants affecting APA.