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

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...

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Detalles Bibliográficos
Autores principales: Zou, Xudong, Ding, Ruofan, Chen, Wenyan, Wang, Gao, Cheng, Shumin, Wang, Qin, Li, Wei, Li, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304671/
https://www.ncbi.nlm.nih.gov/pubmed/35874472
http://dx.doi.org/10.1016/j.xpro.2022.101566
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author Zou, Xudong
Ding, Ruofan
Chen, Wenyan
Wang, Gao
Cheng, Shumin
Wang, Qin
Li, Wei
Li, Lei
author_facet Zou, Xudong
Ding, Ruofan
Chen, Wenyan
Wang, Gao
Cheng, Shumin
Wang, Qin
Li, Wei
Li, Lei
author_sort Zou, Xudong
collection PubMed
description 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).
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spelling pubmed-93046712022-07-23 Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci Zou, Xudong Ding, Ruofan Chen, Wenyan Wang, Gao Cheng, Shumin Wang, Qin Li, Wei Li, Lei STAR Protoc Protocol 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). Elsevier 2022-07-19 /pmc/articles/PMC9304671/ /pubmed/35874472 http://dx.doi.org/10.1016/j.xpro.2022.101566 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Zou, Xudong
Ding, Ruofan
Chen, Wenyan
Wang, Gao
Cheng, Shumin
Wang, Qin
Li, Wei
Li, Lei
Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title_full Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title_fullStr Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title_full_unstemmed Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title_short Using population-scale transcriptomic and genomic data to map 3′ UTR alternative polyadenylation quantitative trait loci
title_sort using population-scale transcriptomic and genomic data to map 3′ utr alternative polyadenylation quantitative trait loci
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304671/
https://www.ncbi.nlm.nih.gov/pubmed/35874472
http://dx.doi.org/10.1016/j.xpro.2022.101566
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