@article{Parkinson_N-2020_9467, title = {Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19}, author = {Parkinson, N. and Rodgers, N. and Fourman, M. and Wang, B. and Zechner, M. and Swets, M. and Millar, J. and Law, A. and Russell, C. and Baillie, J. and Clohisey, S.}, month = {dec}, year = {2020}, 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.}, volume = {10}, issue = {1}, journal = {Scientific Reports}, publisher = {Springer Nature}, url = {https://doi.org/10.1038/s41598-020-79033-3}, }