Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19

Abstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nicholas Parkinson, Natasha Rodgers, Max Head Fourman, Bo Wang, Marie Zechner, Maaike C. Swets, Jonathan E. Millar, Andy Law, Clark D. Russell, J. Kenneth Baillie, Sara Clohisey
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/023f6bf2b1e744f4911b47679a2be540
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:023f6bf2b1e744f4911b47679a2be540
record_format	dspace
spelling	oai:doaj.org-article:023f6bf2b1e744f4911b47679a2be5402021-12-02T11:57:58ZDynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-1910.1038/s41598-020-79033-32045-2322https://doaj.org/article/023f6bf2b1e744f4911b47679a2be5402020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79033-3https://doaj.org/toc/2045-2322Abstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19 . As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies.Nicholas ParkinsonNatasha RodgersMax Head FourmanBo WangMarie ZechnerMaaike C. SwetsJonathan E. MillarAndy LawClark D. RussellJ. Kenneth BaillieSara ClohiseyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
description	Abstract The increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19 . As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies.
format	article
author	Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey
author_facet	Nicholas Parkinson Natasha Rodgers Max Head Fourman Bo Wang Marie Zechner Maaike C. Swets Jonathan E. Millar Andy Law Clark D. Russell J. Kenneth Baillie Sara Clohisey
author_sort	Nicholas Parkinson
title	Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
title_short	Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
title_full	Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
title_fullStr	Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
title_full_unstemmed	Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
title_sort	dynamic data-driven meta-analysis for prioritisation of host genes implicated in covid-19
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/023f6bf2b1e744f4911b47679a2be540
work_keys_str_mv	AT nicholasparkinson dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT natasharodgers dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT maxheadfourman dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT bowang dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT mariezechner dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT maaikecswets dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT jonathanemillar dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT andylaw dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT clarkdrussell dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT jkennethbaillie dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19 AT saraclohisey dynamicdatadrivenmetaanalysisforprioritisationofhostgenesimplicatedincovid19
_version_	1718394729109913600

Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19

Ejemplares similares