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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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). |
format | Online Article Text |
id | pubmed-9304671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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