Systematic comparison of ranking aggregation methods for gene lists in experimental results (preprint)

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.

A common TMPRSS2 variant protects against severe COVID-19 (preprint)

Infection with SARS-CoV-2 has a wide range of clinical presentations, from asymptomatic to life-threatening. Old age is the strongest factor associated with increased COVID19-related mortality, followed by sex and pre-existing conditions. The importance of genetic and immunological factors on COVID19 outcome is also starting to emerge, as demonstrated by population studies and the discovery of damaging variants in genes controlling type I IFN immunity and of autoantibodies that neutralize type I IFNs. The human protein transmembrane protease serine type 2 (TMPRSS2) plays a key role in SARS-CoV-2 infection, as it is required to activate the virus spike protein, facilitating entry into target cells. We focused on the only common TMPRSS2 non-synonymous variant predicted to be damaging (rs12329760), which has a minor allele frequency of [~]25% in the population. In a large population of SARS-CoV-2 positive patients, we show that this variant is associated with a reduced likelihood of developing severe COVID19 (OR 0.87, 95%CI:0.79-0.97, p=0.01). This association was stronger in homozygous individuals when compared to the general population (OR 0.65, 95%CI:0.50-0.84, p=1.3x10-3). We demonstrate in vitro that this variant, which causes the amino acid substitution valine to methionine, impacts the catalytic activity of TMPRSS2 and is less able to support SARS-CoV-2 spike-mediated entry into cells. TMPRSS2 rs12329760 is a common variant associated with a significantly decreased risk of severe COVID19. Further studies are needed to assess the expression of the TMPRSS2 across different age groups. Moreover, our results identify TMPRSS2 as a promising drug target, with a potential role for camostat mesilate, a drug approved for the treatment of chronic pancreatitis and postoperative reflux esophagitis, in the treatment of COVID19. Clinical trials are needed to confirm this.

Genetic mechanisms of critical illness in Covid-19 (preprint)

The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases. Since susceptibility to life-threatening infections and immune-mediated diseases are both strongly heritable traits, we reasoned that host genetic variation may identify mechanistic targets for therapeutic development in Covid-19. GenOMICC (Genetics Of Mortality In Critical Care, genomicc.org) is a global collaborative study to understand the genetic basis of critical illness. Here we report the results of a genome-wide association study (GWAS) in 2790 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Random controls were drawn from three distinct UK population studies. We identify and replicate several novel genome-wide significant associations including variants chr19p13.3 (rs2109069, P = 3.98 x 10-12), within the gene encoding dipeptidyl peptidase 9 (DPP9), and at chr21q22.1 (rs2236757, P = 4.99 x 10-8) in the interferon receptor IFNAR2. Consistent with our focus on extreme disease in younger patients with less comorbidity, we detect a stronger signal at the known 3p21.31 locus than previous studies (rs73064425, P = 1.2 x 10-27). We identify potential targets for repurposing of existing licensed medications. Using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease. Transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19. We detected genome-wide significant gene-level associations for genes with central roles in viral restriction (OAS1, OAS2, OAS3). These results identify specific loci associated with life-threatening disease, and potential targets for host-directed therapies. Randomised clinical trials will be necessary before any change to clinical practice.

Systematic review and meta-analysis identifies potential host therapeutic targets in COVID-19. (preprint)

An increasing body of literature describes the role of host factors in COVID-19 pathogenesis. There is a need to combine diverse, multi-omic data in order to evaluate and substantiate the most robust evidence and inform development of future therapies. We conducted a systematic review of experiments identifying host factors involved in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). Gene lists from these 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. 5,418 genes implicated in human betacoronavirus infection were identified from 32 datasets. The top ranked gene was *PPIA*, encoding cyclophilin A. Pharmacological inhibition with cyclosporine in vitro exerts antiviral activity against several coronaviruses including SARS-CoV. Other highly-ranked genes included proposed prognostic factors (*CXCL10*, *CD4*, *CD3E*) and investigational therapeutic targets (*IL1A*) for COVID-19, but also previously overlooked genes with potential as therapeutic targets. 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. Pathways enriched in gene rankings included T-cell receptor signalling, protein processing, and viral infections. We identified limited overlap of our gene list with host genes implicated in ARDS (innate immune and inflammation genes) and Influenza A virus infection (RNA-binding and ribosome-associated genes). We will continue to update this dynamic ranked list of host genes as the field develops, as a resource to inform and prioritise future studies. Updated results are available at https://baillielab.net/maic/covid19.