Disease Studies


At the beginning of the current pandemic, we offered the use of our PrecisionLife® platform to the global research community to analyze large scale, multi-omic, clinical and epidemiological data for COVID-19 datasets.

Since then we have worked with several international groups around our analysis results. We have generated multiple insights into the genetics, clinical presentation, pathology and treatment of COVID-19 patients that would not have been possible with existing analysis tools. Links to preprints and publications can be found below and a comparison of findings achieved with other methods.

We continue to run new studies and to post new pre-prints and manuscripts. We have placed all of our COVID-19 results into the public domain. This has led us to working closely with scientists and clinicians battling the COVID-19 pandemic across the US, Denmark, Germany, Taiwan, and the UK.

If you’re interested in collaborating with us, have a COVID-19 dataset or would like more information on previous studies, please contact us at covid-19@precisionlife.com.

Previous papers / study results

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

February 11, 2021.

We replicated previous findings around the potential role of calcium and lipid homeostasis in severe COVID-19 using clinical and claims data.

We would like to acknowledge the UnitedHealth Group for providing us access to the COVID-19 Data Suite
through the UHG Clinical Discovery Portal and the patients who provided their data.

View the study

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

June 25, 2020

We used a UK Biobank population designated with mild or severe COVID-19 to identify 68 genes associated with risk of developing a severe clinical response to SARS-CoV-2.

View the study

Shared genetic risk factors between sepsis and COVID-19

May 5, 2020

Our first COVID-19 study identified shared genetic risk factors for sepsis (a common clinical co-morbidity with severe COVID-19) and 59 drug repurposing candidates.

View the study

Current studies

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

Manuscript in press

Systematic analysis and mapping of drug repurposing experiments to integrated disease maps for SARS-CoV-2 / COVID-19.

View the study


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.

Long COVID study

Genomics England

PrecisionLife is using the Genomics England Research Environment to evaluate the factors driving differential clinical outcomes (severity and longevity of disease) in a COVID-19 population.

Comparison with other studies

During the pandemic, a number of global science efforts of COVID-19 host susceptibility analysis were undertaken by leading groups. We believe that our analytics find more useful, proprietary insights than others.

Study Case Controls Loci Mechanisms Assoc. genes Druggable targets Drug repurposing candidates Replicated in clinical data
PrecisionLife 725 1450 158 6+ 88 23 59 yes*
23andMe 1,131 15,434 11 1 1
COVID-19 host genetics initiative 13,641 2,070,709 15 3 9

* We would like to acknowledge the UnitedHealth Group for providing us access to the COVID-19 Data Suite through the UHG Clinical Discovery Portal and the patients who provided their data.


Using combinatorial analysis we can find additional signal in patient datasets that is invisible to existing GWAS and other genetic analysis methods[1][2][3]. This additional power is illustrated by a meta-analysis of the various large-scale studies that have been performed into the genetic factors underpinning COVID-19 host response as it relates to disease susceptibility and severity.

COVID-19 surprised the medical community by the range of its symptoms. Rather than a pure viral infection and inflammasome/cytokine response, the disease has had widespread effects across a range of tissues[4][5]. As data became available at the beginning of the pandemic, GWAS analysis of very large patient populations were run by various groups.

A GWAS study involving 1,131 severe patients and 15,434 mild controls identified one locus associated with high risk of developing severe COVID-19 around the ABO blood group gene and another locus on chromosome 3[6]. This study was then extended in a global effort that ran GWAS on genomic data from 13,641 severe disease patients and over 2 million controls, identifying four genome-wide significant loci that are associated with SARS-CoV-2 infection and 11 associated with severe manifestations of COVID-19[7][8].

Most of these correspond to previously documented genes with associations to lung or autoimmune and inflammatory diseases. The symptomology of COVID-19 however extends much further than can be explained by these findings into neurological, coagulation and cardiovascular, renal and other consequences beyond inflammation driven disease.

In contrast, a PrecisionLife study using a combinatorial analytics approach had been run several months earlier on the very first COVID-19 datasets available from UK Biobank[9][10], with just a few hundred severe patients, while controlling for all the existing known predisposing co-morbidities[11]. This study, working off much smaller datasets than the GWAS studies, identified 156 severe disease associated loci that mapped to 68 protein coding genes, spread across a range of mechanisms.

Many novel targets were identified that are involved in key severe COVID-19 pathology and mechanisms, including production of pro-inflammatory cytokines, endothelial cell dysfunction, lipid droplets, neurodegeneration and viral susceptibility factors. The novel disease associated mechanisms identified in this genetics based study were replicated and 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 [12]. Several of the novel targets have also been subsequently validated in collaborative studies into drug repurposing using viral plaque assays and other disease models[13][14].

[1] Mellerup E, Andreassen O, Bennike B, et al. Connection between genetic and clinical data in bipolar disorder PLoS One. 2012;7(9):e44623. doi:10.1371/journal.pone.0044623

[2] Mellerup E, Møller GL. Combinations of Genetic Variants Occurring Exclusively in Patients. Comput Struct Biotechnol J. 2017 Mar 10;15:286-289. doi: 10.1016/j.csbj.2017.03.001.

[3] Tam V et al, Benefits and limitations of genome-wide association studies, Nat Rev Genet: 2019

[4] Jain U. Effect of COVID-19 on the Organs. Cureus. 2020;12(8):e9540. Published 2020 Aug 3. doi:10.7759/cureus.9540

[5] Rando HM, Bennett TD, Byrd JB, et al. Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information. Preprint. medRxiv. 2021;2021.03.20.21253896. Published 2021 Mar 26. doi:10.1101/2021.03.20.21253896

[6] Shelton, JF, Shastri AJ, Ye, C et al. Trans-ethnic analysis reveals genetic and non-genetic associations with COVID-19 susceptibility and severity medRxiv 2020.09.04.20188318; doi: https://doi.org/10.1101/2020.09.04.20188318

[7] Pairo-Castineira, E., Clohisey, S., Klaric, L. et al. Genetic mechanisms of critical illness in COVID-19. Nature 591, 92–98 (2021). https://doi.org/10.1038/s41586-020-03065-y

[8] Severe Covid-19 GWAS Group, Ellinghaus D, Degenhardt F, et al. Genomewide Association Study of Severe Covid-19 with Respiratory Failure. N Engl J Med. 2020;383(16):1522-1534. doi:10.1056/NEJMoa2020283

[9] Sudlow C, Gallacher J, Allen N, Beral V, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015 Mar 31;12(3):e1001779. doi: 10.1371/journal.pmed.1001779.

[10] Armstrong, J., Rudkin, J. K., Allen, N., Crook, D. W., Wilson, D. J., Wyllie, D. H. and A. M. O’Connell Dynamic linkage of COVID-19 test results between Public Health England’s Second Generation Surveillance System and UK Biobank (2020) Microbial Genomics doi:10.1099/mgen.0.000397

[11] Taylor K, Das S, Pearson M, Kozubek J, Pawlowski M, Jensen CE, Skowron Z, Møller GL, Strivens MA, Gardner SP Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients medRxiv 2020.06.17.20134015; doi: https://doi.org/10.1101/2020.06.17.20134015

[12] Das S, Pearson M, Taylor K, Bouchet VA, Møller GL, Hall TO, Strivens MA, Tzeng KTH, Gardner SP Combinatorial analysis of phenotypic and clinical risk factors associated with hospitalized COVID-19 patients (in press) medRxiv 2021.02.08.21250899; doi: https://doi.org/10.1101/2021.02.08.21250899

[13] Hofmann-Apitius M et al. The COVID-19 PHARMACOME: A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization (in press) Fraunhofer Institute for Algorithms and Scientific Computing DE

[14] 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: https://doi.org/10.1101/2021.04.13.439274

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