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

Nicholas Parkinson, Natasha Rodgers, Max Head Fourman, Bo Wang, Marie Zechner, Maaike C. Swets, Jonathan Millar, Andy Law, Clark D Russell, J Kenneth Baillie, Sara Clohisey

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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
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
Original languageEnglish
JournalScientific Reports
Issue number1
Early online date18 Dec 2020
Publication statusE-pub ahead of print - 18 Dec 2020


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