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Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data

Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs...

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Detalles Bibliográficos
Autores principales: Hoskins, Jason W., Christensen, Trevor A., Amundadottir, Laufey T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285694/
https://www.ncbi.nlm.nih.gov/pubmed/37330907
http://dx.doi.org/10.1016/j.xpro.2023.102362
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author Hoskins, Jason W.
Christensen, Trevor A.
Amundadottir, Laufey T.
author_facet Hoskins, Jason W.
Christensen, Trevor A.
Amundadottir, Laufey T.
author_sort Hoskins, Jason W.
collection PubMed
description Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs). We describe steps for tissue-/cell-type-specific co-expression network modeling, expression regulator activity inference, and identification of representative phenotypic master regulators. Finally, we detail activity QTL and eQTL analyses. This protocol requires genotype, expression, and relevant covariables and phenotype data from existing eQTL datasets. For complete details on the use and execution of this protocol, please refer to Hoskins et al.(1)
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spelling pubmed-102856942023-06-23 Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data Hoskins, Jason W. Christensen, Trevor A. Amundadottir, Laufey T. STAR Protoc Protocol Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs). We describe steps for tissue-/cell-type-specific co-expression network modeling, expression regulator activity inference, and identification of representative phenotypic master regulators. Finally, we detail activity QTL and eQTL analyses. This protocol requires genotype, expression, and relevant covariables and phenotype data from existing eQTL datasets. For complete details on the use and execution of this protocol, please refer to Hoskins et al.(1) Elsevier 2023-06-17 /pmc/articles/PMC10285694/ /pubmed/37330907 http://dx.doi.org/10.1016/j.xpro.2023.102362 Text en 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
Hoskins, Jason W.
Christensen, Trevor A.
Amundadottir, Laufey T.
Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title_full Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title_fullStr Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title_full_unstemmed Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title_short Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data
title_sort master regulator activity qtl protocol to implicate regulatory pathways potentially mediating gwas signals using eqtl data
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285694/
https://www.ncbi.nlm.nih.gov/pubmed/37330907
http://dx.doi.org/10.1016/j.xpro.2023.102362
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