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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Alternative polyadenylation (APA) in 3' untranslated regions (UTRs) influences gene expression and is linked to various traits.
• Genetic variants in non-coding regions are challenging to interpret, yet play a significant role in trait heritability.
• Existing quantitative trait loci (QTL) studies often overlook APA events.

Purpose of the Study:

• To present a standardized bioinformatic protocol for identifying 3' UTR alternative polyadenylation quantitative trait loci (3'aQTLs).
• To enable researchers to analyze dynamic APA events and their genetic underpinnings.
• To facilitate the prediction of causal genetic variants affecting APA.

Main Methods:

• Utilizes standard RNA-sequencing (RNA-seq) data.
• Integrates matched genomic data for variant association analysis.
• Employs bioinformatic pipelines to detect differential 3' UTR usage and identify associated genetic variants.

Main Results:

• The 3'aQTLs identified can explain a substantial portion (16.1%) of trait-associated non-coding variants.
• The identified 3'aQTLs are largely distinct from other molecular QTLs, highlighting APA's unique role.
• The protocol successfully identifies genetic variants associated with differential 3' UTR usage.

Conclusions:

• The described protocol provides a robust framework for investigating the genetic basis of APA.
• This approach enhances the understanding of non-coding variant function in gene regulation.
• The findings underscore the importance of APA in explaining genetic contributions to complex traits.