Dataset Information

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

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.

SUBMITTER: Ma X

PROVIDER: S-EPMC9825496 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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

Ma Xuelian X Cheng Shumin S Ding Ruofan R Zhao Zhaozhao Z Zou XuDong X Guang Shouhong S Wang Qixuan Q Jing Huan H Yu Chen C Ni Ting T Li Lei L

Nucleic acids research 20230101 D1

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 o ...[more]

PMID: 36043442

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Project description:BackgroundIn order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants.ResultsOur approach has 1) a distinct computational edge, and 2) comparable performance in terms of statistical power over other currently existing joint modeling approaches such as MetaTissue eQTL and eQTL-BMA. Using simulations, we show that our method increases the power to detect eQTLs when compared to a tissue-by-tissue approach and can exceed the performance, in terms of computational speed, of MetaTissue eQTL and eQTL-BMA. We apply our method to two publicly available expression datasets from normal human brains, one comprised of four brain regions from 150 neuropathologically normal samples and another comprised of ten brain regions from 134 neuropathologically normal samples, and show that by using our method and jointly analyzing multiple brain regions, we identify eQTLs within more genes when compared to three often used existing methods.ConclusionsSince we employ a score test-based approach, there is no need for parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Our method also accommodates the analysis of next- generation sequencing data. As an example, by modeling gene transcripts in an analogous fashion to tissues in our current formulation one would be able to test for both a variant overall effect across all isoforms of a gene as well as transcript-specific effects. We implement our approach within the R package JAGUAR, which is now available at the Comprehensive R Archive Network repository.

Dataset Information

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

Publications

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

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