Mapping 3′UTR alternative polyadenylation quantitative trait loci through population-scale transcriptomic and genomic data

Abbreviation

• APA: alternative polyadenylation
• 3'aQTL: 3′UTR alternative polyadenylation quantitative trait loci

This pipeline describes the detailed steps for analyzing dynamics alternative polyadenylation events across lymphoblastoid cell lines (LCL) samples from 445 unrelated individuals and performing association analysis between common genetic variants and APA events to obtain a map of genetic regulation of APA. The whole pipeline includes APA quantitative analysis across samples, association test between common genetic variants and APA usage (mapping 3'aQTL), fine-mapping 3'aQTLs, and other steps for preparing phenotype, genotype data and processing output of above analyses. The final outputs of this pipeline including the matrix of APA usage profile across samples, the table of association between common genetic variants and APA usage (3'aQTLs), the table of fine-mapped 3'aQTLs.

The scripts in this repository were tested on Geuvadis RNA-seq Project dataset which contains RNA sequencing data of 445 unrelated individuals (belong to five sub-populations, ~90 samples in each) and corresponding genotype data from 1000 Genome Project. We recommend analyzing each of the five sub-populations separately.

For conditions to reuse of these scripts please refer to LICENSE file.

Using this pipeline

Details on how to prepare environment and use the script can be found on GitHub wiki pages for this repository.

This pipeline relies on Dapars2 for APA quantification, Matrix-eQTL for association mapping, and SuSieR for fine-mapping.

Authors

Xudong Zou, Ruofan Ding, Wenyan Chen, Gao Wang, Shumin Cheng, Wei Li, Lei Li

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China

Citation

• Code and Execution:

[Ref TBD]

• The first 3'aQTL atlas of human tissues:

An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability

Lei Li, Kai-Lieh Huang, Yipeng Gao, Ya Cui, Gao Wang, Nathan D. Elrod, Yumei Li, Yiling Elaine Chen, Ping Ji, Fanglue Peng, William K. Russell, Eric J. Wagner & Wei Li. Nature Genetics,53,994-1005 (2021). DOI:https://doi.org/10.1038/s41588-021-00864-5

https://www.nature.com/articles/s41588-021-00864-5

Contact

For any issues, please create a GitHub Issue.

Funding

This work was supported by National Natural Science Foundation of China (no. 32100533) and startup funds from Shenzhen Bay Laboratory to L.L.