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 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 ranked genes 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 list of genes as a resource to inform and prioritise future
studies.

Rights

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Cite as

Downloadable citations

Identifiers

DOI

https://doi.org/10.1038/s41598-020-79033-3

Repository URI

https://www.research.ed.ac.uk/portal/en/publications/dynamic-datadriven-metaanalysis-for-prioritisation-of-host-genes-implicated-in-covid19(1770d2f6-0065-4f2e-9af7-0858f3a4978d).html