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. 2023 Jan 6;51(D1):D1046-D1052.
doi: 10.1093/nar/gkac736.

ipaQTL-atlas: an atlas of intronic polyadenylation quantitative trait loci across human tissues

Xuelian Ma  1 , Shumin Cheng  1 , Ruofan Ding  1 , Zhaozhao Zhao  2 , XuDong Zou  1 , Shouhong Guang  3 , Qixuan Wang  1 , Huan Jing  4 , Chen Yu  5 , Ting Ni  2 , Lei Li  1
Affiliations

ipaQTL-atlas: an atlas of intronic polyadenylation quantitative trait loci across human tissues

Xuelian Ma et al. Nucleic Acids Res. .

Abstract

Functional interpretation of disease-associated non-coding variants remains a significant challenge in the post-GWAS era. Our recent study has identified 3'UTR alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) and connects APA events with QTLs as a major driver of human traits and diseases. Besides 3'UTR, APA events can also occur in intron regions, and increasing evidence has connected intronic polyadenylation with disease risk. However, systematic investigation of the roles of intronic polyadenylation in human diseases remained challenging due to the lack of a comprehensive database across a variety of human tissues. Here, we developed ipaQTL-atlas (http://bioinfo.szbl.ac.cn/ipaQTL) as the first comprehensive portal for intronic polyadenylation. The ipaQTL-atlas is based on the analysis of 15 170 RNA-seq data from 838 individuals across 49 Genotype-Tissue Expression (GTEx v8) tissues and contains ∼0.98 million SNPs associated with intronic APA events. It provides an interface for ipaQTLs search, genome browser, boxplots, and data download, as well as the visualization of GWAS and ipaQTL colocalization results. ipaQTL-atlas provides a one-stop portal to access intronic polyadenylation information and could significantly advance the discovery of APA-associated disease susceptibility genes.

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Figures

Figure 1.
Figure 1.
Data processing and data statistics in ipaQTL-atlas. (A) Workflows of ipaQTL-atlas. IPA with the arrow denotes the position of the corresponding intronic polyadenylation site. WGS, whole-genome sequencing; ipaQTLs, intronic polyadenylation quantitative trait loci. (B) The number of RNA-seq samples for each tissue used in the ipaQTL-atlas was distributed. (C) The distribution of the number of IpA events and significant ipaQTLs SNPs (FDR ≤ 0.05) for each tissue was sorted by the tissue sample sizes. Each color indicates a tissue of origin.
Figure 2.
Figure 2.
The web interface of ipaQTL-atlas. (A) ipaQTLs query interface and result visualization for ‘Gene/SNP Search’. (B) An example of the genome browser view shows the ipaQTLs of adipose subcutaneous tissue at the IVD locus. (C) The interface of ‘ipaQTLs Boxplots’ and an example of the ipaQTLs boxplot for ARPC2 and rs13392177 in brain hippocampus. (D) The interface of the ‘GWAS-ipaQTLs Colocalization’ and an example of the LocusCompare plot at the intronic region of the PMS2P3 locus with T2D GWAS P-values and ipaQTLs P-values in colon sigmoid tissue. (E) The interface of the ‘Traits/Diseases’ and an example of the ipaQTLs with Alzheimer’s disease in brain frontal cortex BA9 tissue.
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