Dataset Information

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

ABSTRACT: 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).

SUBMITTER: Zou X

PROVIDER: S-EPMC9304671 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zou Xudong X Ding Ruofan R Chen Wenyan W Wang Gao G Cheng Shumin S Wang Qin Q Li Wei W Li Lei L

STAR protocols 20220719 3

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 affe ...[more]

PMID: 35874472

Similar Datasets

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.

Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets