Skip to content

COVID-19 Disease Study

Generating more insights, faster, from smaller datasets


novel gene targets identified - 70% of these are now independently validated


opportunities for approved drugs and candidates that could be repurposed as COVID-19 treatments


of these drug candidates evaluated in clinical trials


As the first wave of the COVID-19 pandemic surged around the world in early 2020, scientists and clinicians united in an unprecedented effort to understand the disease, its diverse symptoms and severities, and how to treat it. As part of this effort, we analyzed genetic host response risk factors in the first small cohort of 779 severe COVID-19 patients reported from the UK Biobank.

Using our unique combinatorial analytics platform, we identified drivers of different aspects of disease biology in specific patient subgroups and generated insights into the genetics, clinical presentation, pathology, and treatment of COVID-19 patients. The level of insights generated are not achievable with other analysis tools.


Understanding COVID-19 and finding novel drug targets

The SARS-CoV-2 virus posed an unprecedented new threat to world health, and in early 2020 the clinical implications for patients were unclear. There were no approved vaccines for COVID-19, and the only genetic associations identified for severe COVID-19 risk were linked to the ABO gene and a narrow region on chromosome 31. The medical community scrambled to understand the range of symptoms the disease caused and why some patients only presented with mild symptoms, but others developed life-threatening disease.

We seized the opportunity to analyze the first small cohort of 779 severe COVID-19 patients reported in the UK Biobank. We wanted to shed light on which host genes were implicated with the most severe forms of the disease and why there were so many different symptoms.

Novel disease-associated mechanisms that we identified were validated by subsequent combinatorial analysis of the de-identified patient health records (non-genetic data comprising longitudinal diagnosis, claims, and lab data) in the UnitedHealth Group COVID-19 Data Suite. Several of the novel targets have also been subsequently validated in collaborative studies into drug repurposing using viral plaque assays and other disease models2,3.

COVID 19 Disease Architecture

12 genes associated with host immune response to virus infection

5 genes associated with regulation of inflammatory cytokines

5 genes for lipid storage, signaling and droplet production

12 genes associated with cardiovascular and endothelial cell functions

9 genes associated with Wnt/B-catenin signaling

6 genes associated with neurodegenerative diseases 

Druggable targets associated with ARDS, sepsis and other life-threatening complications

Download poster

Outcomes and validation

Our approach outperformed global efforts to identify genetic factors for severe COVID-19 susceptibility, producing more proprietary insights, novel clinical biomarkers, and novel drug targets.

Our first COVID-19 analysis was completed 12 months earlier than COVID-19 HGI – within 3 weeks of the first UK Biobank dataset on just over 700 patients becoming available.

Our AI-led combinatorial analytics platform identified many more loci and genes associated with severe COVID-19, far exceeding the results from global research collaborations that had more time, resource, and data. We were the first to report 68 genes associated with severe disease and linked to all the major symptoms. These findings (including 48 of the genes and 13 drug repurposing candidates) have since been extensively validated by our collaborators and independently by other groups around the world.

Comparison of global efforts to identify genetic factors for severe COVID-19 susceptibility




United Healthcare


Optum Labs


UK Biobank


There are already a number of published papers/reprints, covering our work on Covid-19 :

Publications and Collaborators - frontiers

Combinatorial analysis of phenotypic and clinical risk factors associated with hospitalized COVID-19 patients

Publications and Collaborators-nature

The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization

Publications and Collaborators-medrxiv

Our initial COVID-19 study identified shared genetic risk factors in sepsis and high mortality risk patients plus 59 drug repurposing candidates

Publications and Collaborators-medrxiv

Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients

Ongoing studies



PrecisionLife is working with Sano Genetics to identify genetic, clinical and epidemiological factors associated with long COVID in a 3,000 patient COVID-19 population.



  1. Shelton, J.F., Shastri, A.J., Ye, C. et al. Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity. Nat Genet 53, 801–808 (2021).

  2. Schultz, B., Zaliani, A., Ebeling, C. et al. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Sci Rep 11,11049 (2021); doi:

  3. Sugiyama MG, Cui H, Redka DS, Karimzadeh M et al. Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease. bioRxiv 2021.04.13.439274; doi:

  4. Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients. (2020)

  5. Das, S., Pearson, M., Taylor, K. et al. (2021) Combinatorial analysis of phenotypic and clinical risk factors associated with hospitalized COVID-19 patients Front. Digit. Health, 08 July 2021 |

  6. COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature (2021)

Discuss our Covid-19 Disease Study

Contact us