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

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Sci Rep


. 2020 Dec 18;10(1):22303.
doi: 10.1038/s41598-020-79033-3.
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19


Nicholas Parkinson ¹ , Natasha Rodgers ¹ , Max Head Fourman ¹ , Bo Wang ¹ , Marie Zechner ¹ , Maaike C Swets ^{1 2} , Jonathan E Millar ¹ , Andy Law ¹ , Clark D Russell ^{1 3} , J Kenneth Baillie ⁴ , Sara Clohisey ⁵



Affiliations

• PMID: 33339864
• DOI: 10.1038/s41598-020-79033-3

Free article

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.