The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 219,692 patients with COVID-19 from 81 studies across 35 countries. In the latest release (April, 2022), we report 51 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

The next step in our global fight against coronavirus diseases is to develop new therapeutic strategies with broad antiviral activity in preparation for future strains or severe variants. Presumably, host-based proteins may offer an alternative targeting approach with a high barrier to drug resistance. Here, we identify novel COVID-19 host targets and drug repurposing candidates, using two complementary machine learning technologies. Non-obvious, host-based targets were first identified using a Graph Convolutional Network (GCN) trained to model clinically relevant network proximity distances in a multiscale interactome. The multiscale interactome combines relationships between genes, proteins, drugs, biological pathways and disease, including viral-host protein-protein interactions networks. Additional drug repurposing candidates were then retrieved from PolypharmDB for several GCN-identified host targets. PolypharmDB is a database of 10,244 clinically-tested drugs cross-screened with 8535 human proteins, based on a deep learning Drug Target Interaction (DTI) prediction model. Twenty-six FDA-approved drugs identified by this screen were selected for cellular infectivity assays, of which four had demonstrable bioactivities. One notable hit demonstrated potent antiviral activity against five genetically different human coronaviruses, while its predicted target was also confirmed by siRNA gene silencing. Together, we present a promising drug repurposing candidate and a new therapeutic target for future drug design programs.

Over the past 14 years the global biology community has captured and shared over 30 petabases (3x1016 nucleobases) of Earth’s genetic sequence data. These millions of samples cover all continents, oceans, thousands of animals and plant species, deep wilderness environments, and are valued at $3.6+ billion dollars.
To search for the (evolutionary) origins of SARS-CoV-2 and uncover Earth’s RNA viral diversity, the open science Serratus team analyzed 5.7 million sequencing libraries. In a single 11-day experiment we identified >130,000 novel species of RNA viruses; this is an approximately an order of magnitude increase over all 15,000 known RNA viruses that were described up until that point, including nine new species of Coronaviruses.
We are rapidly progressing towards the exabase era of computional biology, thus this near order-of-magnitude increase in the RNA virome is superficial, merely a starting point to more fundamental questions: What lessons did COVID19 teach us for constructing a planetary-scale pandemic warning systems? How are we going to interpret the ~100 million RNA viruses which will be discovered by the end of the decade?