The unprecedented response: An ecosystem of precision medicine research teams fighting Covid-19

by Paulo Andre, MD

COVID-19 was declared a worldwide pandemic by the World Health Organization (WHO) on March 11, 2020.1  Since appearing first in humans in China back in late 2019, the virus has now spread to more than 200 countries and resulted in over a half a million deaths and counting. The pandemic has wreaked havoc on global health care systems and economies, while drastically changing the way people live. The response to this crisis  by the scientific community and the general public is truly unprecedented. Scientists from around the world have joined together in unique collaborations to better understand the biology of the disease and who is most at risk, while other individuals without any scientific background are participating in innovative research to support the discovery of effective treatments and vaccines.

As cases of COVID-19 continue to spread throughout the world, it is clear there is a wide range in infection severity. Some patients are asymptomatic or experience very mild symptoms, but others can end up in intensive care with acute respiratory failure. Evidence suggests advanced age and underlying chronic health conditions such as heart disease, pulmonary disease, diabetes and compromised immune systems pose a greater risk of severe illness.2 However, the virus has also been shown to cause severe illness and death in younger patient populations without any preexisting conditions. What factors determine if someone will have a mild infection versus a fatal one? Many researchers are trying to answer this question by examining the genetic susceptibility across populations to being infected by, and expressing symptoms for, COVID-19.3

COVID-19 Host Genetics Initiative

The COVID-19 Host Genetics Initiative was established at the onset of the pandemic to promote research and data sharing across countries regarding the role of genetics in COVID-19 infections. Andrea Ganna of the University of Helsinki is one of the lead researchers behind this consortium. He says it has brought together an international network of researchers, scientists and geneticists from more than 50 nations, ranging from large academic research centers to individual clinician researchers who are not necessarily involved in host genetics but have data that they want to contribute to the research.4

The initiative’s mission is to “generate, share and analyze data to learn the genetic determinants of COVID-19 susceptibility, severity and outcomes”.5 This is an innovative approach where host genetics research is aggregated and shared in a timely and transparent manner. The initiative’s analysis plan specifies it will include a diverse scope of data types, including genome-wide association studies (GWAS), whole exome sequencing (WES) and whole genome sequencing (WGS).6

According to an article published in the European Journal of Human Genetics, the COVID-19 Host Genetics Initiative allows scientists to register on the initiative’s website to submit individual level data or summary statistics and encourages real-time communication among researchers via a Slack platform with support from the International Common Disease Alliance.7  The article points out that data can be generated from retrospective collections (e.g., biobanks with existing genetic data affiliated with health systems) or prospective collections directly from COVID-19 patients and analysis results are made available on the website for the benefit of the entire scientific community. There is a protocol to share data statistics across research teams as shown on Figure 1. Over 200 partners have joined the COVID-19 Host Genetics Initiative.8

COVID19-Host(a)ge  GWAS  Study

A genome-wide association (GWAS) study published in the New England Journal of Medicine found that gene variants in two different regions of the human genome were associated with severe COVID-19.9 These findings came from a team led by Andre Franke, a scientist at Christian-Albrecht-University, Kiel, Germany, along with Tom Karlsen, Oslo University Hospital Rikshospitalet, Norway. Their study included 1,980 people who were treated for severe COVID-19 and respiratory failure at seven medical centers in Italy and Spain. Their analysis identified the 3p21.31 gene cluster (including SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1) as a genetic susceptibility locus in severe patients with COVID-19 and respiratory failure. The same study also identified at locus 9q34.2 an association signal that coincided with the ABO blood group locus. While an association with the 3p21.31 gene cluster was confirmed when data from the GWAS study was combined with data from other studies participating in the COVID-19 Host Genetics Initiative, an association with the ABO blood group locus could not be confirmed as shown on the Manhattan plot in Figure 2 and Figure 3.10  A Massachusetts General Hospital study published in the Annals of Hematology also found no relationship between blood type and severity of COVID-19 infection.11

The COVID-19 Host Genetics Initiative demonstrates the importance of different teams aggregating their data in order to reach meaningful conclusions. While these early results are promising, more collaborative research is needed to further understand the genetic implications for COVID-19.

MDcovid Crowdsourcing GWAS Study

As a partner with the COVID-19 Host Genetics

Initiative, MDcovid (https://MDcovid.com) shares the goal of identifying how genes influence COVID-19 infection. Unlike other COVID-19 research studies that collect DNA samples at medical centers, MDcovid’s mission is unique in that it provides an opportunity for individuals who have been infected with COVID-19 to volunteer to be DNA genotyped at home or share their previous genetic testing so MDcovid can look for associations  between  specific gene variants and different clinical outcomes. Other COVID-19 crowdsourcing initiatives have proven successful in the fight against this virus, such as a recent study that showed that the combination of loss of smell and taste, fever, persistent cough, fatigue, diarrhea, abdominal pain and loss of appetite is highly predictive of a COVID-19 positive test.12

Using social media, MDcovid targeted specific populations in areas with a high prevalence of COVID-19 infections, including New York, Massachusetts and Portugal. We also interacted with members of Facebook  groups  interested in COVID-19. Individuals wanting to participate must complete a survey with a series of questions, including whether they have pre- existing health conditions, if they have had a COVID-19 infection, how it was diagnosed, what symptoms they had, if they were hospitalized and if they required intensive care or intubation.

Disease severity was classified according to the Disease severity was classified according to the phenotype definitions from the COVID-19 Host Genetics Initiative.13 Participants selected forthe study are also asked if they would take a DNA test at home or if they had previous DNA genotyping done.  So far we have reached roughly 34,000 different people. Approximately 3,000 persons clicked on the link to our COVID-19 questionnaire, 210 completed the survey and 155 agreed to be genotyped or share their previous results. Since research participants were  recruited using social media interactions (e.g., Facebook, Messenger and WhatsApp), it also provided an opportunity to educate the general public about genomics and precision medicine while fighting misinformation about COVID-19.

Our research team is adjusting the GWAS data by age and sex and doing genotype imputation on the Michigan imputation server.14 We are using SAIGE for GWAS analysis as recommended by the COVID-19 Host Genetics Initiative.15 The collection and analysis of DNA data for this project is ongoing, but the prospect of finding new genetic associations in COVID-19 infections by aggregating our results with the other partners on the COVID-19 Host Genetics Initiative is very exciting.

In-silico Studies

The rapid release of data from the COVID-19 Host Genetics Initiative has meant that researchers in different fields have  greater access to data and opportunities to offer further insights. Scientists are now using the raw data generated from the initiative to perform various types of in-silico studies (biological experiments performed with computer simulations) Since the single nucleotide polymorphisms (SNPs) found  so far by the COVID-19 Host Genetics Initiative  at the 3p21.31 gene clusters (including SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, and XCR1) are located in non-coding regions, and given the tight linkage disequilibrium (LD) associated in this area, it’s unclear which one of the genes is related with the severity of a COVID-19 infection. Figure 4 provides a brief summary of what is known about these genes. The hope is that data obtained from further studies will help identify which gene(s) impact COVID-19 severity and why.

TWAS Studies

One of the ways to find out the specific gene is to correlate GWAS data with tissue-specific gene expression data by doing a Transcriptome- Wide Association Study (TWAS). Correlations between genotype and tissue-specific gene expression levels will help identify regions of the genome that influence whether and how much a gene is expressed.

The Genotype-Tissue Expression (GTEx) database contains tissue samples from nearly 1,000 individuals who were genotyped and their RNA expression was quantified across different tissues (see Figure 5). Nicholas Mancuso and his colleagues ran the TWAS software FUSION with the COVID-19 GWAS data using as reference the total messenger RNA abundance in the GTEx lung tissues. They found two gene associations in GTEx Lung: CXCR6 (Receptor for the C-X-C chemokine CXCL16, present on the 3p21.31 gene cluster that were previously flagged as statistically significant on the COVID-19 GWAS study) and IFNAR2 (which encodes an interferon receptor subunit).16

 

Gita Pathak and her colleagues also performed a TWAS analysis on whole blood using a different software platform called MetaXcan. They found significant associations for genes CCR9 (Receptor for chemokine SCYA25/TECK present on the 3p21.31 gene cluster) and BET1L (Bet1 Golgi Vesicular Membrane Trafficking Protein Like).17

It is interesting to note that both studies show that specific chemokine receptors that regulate immune responses seem to be involved in COVID-19 disease severity.

Mendelian randomization studies Schooling and his colleagues used a different method (Mendelian randomization) with the GWAS meta-analysis data and were able to replicate the RECOVERY trial results that provided evidence that treatment with the steroid dexamethasone reduced mortality in patients with Covid-19 who were receiving respiratory support (see Figure 6).18 Schooling was able to provide genetic validation for the use of both tocilizumab and statins in COVID-19, but not anakinra.19

 

Neanderthal DNA Study

Hugo Zeberg, a geneticist at the Karolinska Institute in Sweden and Svante Paabo, the director of the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, realized that the genetic variants on chromosome 3 that increase the risk of a severe COVID-19 infection were non-randomly associated with each other in the general population (high linkage disequilibrium) possibly because of interbreeding between Neanderthals and modern humans ~50,000 years ago.20 Their research points out that this Neanderthal haplotype occurs in South Asia at a frequency of 30% (in Bangladesh at 63%), in Europe at 8% and is almost absent in Africa (consistent with the lack of interbreeding between Neanderthals and African populations).

A previous study showed that Neanderthals and modern humans exchanged viruses, but gene flow from Neanderthal to modern humans helped them adapt against the same viruses.21 An unexpected finding, however, was that certain Neanderthal variants now seem detrimental to people faced with COVID-19.

This dichotomy between the same genetic variant being beneficial in certain circumstances and detrimental in others is not unprecedented. For example, the sickle-cell trait is advantageous for a person living in an area where malaria is endemic, but it can be dangerous with high intensity physical activity or at high altitudes.22

Conclusion

The stakes are high as researchers look for genetic clues to understand who is most susceptible to COVID-19 in hopes of developing effective treatment options and vaccines to protect the public against this deadly virus. As of this writing, over 22 million COVID-19 cases have been confirmed and thousands of people are dying each day. This global challenge is best met by collaborative efforts to accelerate  research and disseminate data. Working together, the international scientific community and individuals from the general public can advance the research needed to develop effective treatments for COVID-19.

Paulo Andre MD leads the MDcovid project and is also the CEO of MDinteractive, a disease registry aimed at helping clinicians monitor  and improve the quality of their patient care by transforming their data into information to enhance decision- making. Prior to forming MDinteractive, Dr. Andre conducted genetic mutations research at the Massachusetts Institute of Technology in Cambridge, MA. He completed his neurology residency at Tufts Medical Center, after which he cared for patients with neurological conditions in the Boston area for many years.

 

 

References

  1. World Health “Timeline of WHO’s response to COVID-19.” World Health Organization, 30 June 2020, https://www.who.int/news-room/detail/29-06-2020-covidtimeline.
  2. Centers for Disease Control and “COVID-19 People with Certain Medical Conditions.” CDC, www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html 2020.
  3. Kaiser, “How sick will the coronavirus make you? The answer may be in your genes.” Science, American Association for the Advancement of Science, 27 Mar. 2020, www.sciencemag.org/news/2020/03/how-sick-will-coronavirus-make-you-answer-may-be-your-genes.
  4. Ganna, Ep 39 How hundreds of scientists from 50 nations are collaborating on Slack to study genetics & COVID19 with Dr Andrea Ganna, The Genetics Podcast, 2020. Apple Podcasts, https://podcasts.apple.com/gb/podcast/%20ep-39-how-hundreds-scientists-from-50-nations-are-collaborating/id1462418412?i=1000483795034
  5. The COVID-19 Host Genetics COVID-19 hg. www.covid19hg.org/.2020.
  6. The COVID-19 Host Genetics “Analysis Plan.” COVID-19 hg, www.covid19hg.org/about/. Apr. 2020.
  7. The COVID-19 Host Genetics “The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic.” European Journal of Human Genetics , Nature, 13 May 2020, www.nature.com/articles/s41431-020-0636-6.
  8. The COVID-19 Host Genetics “Partners.” COVID-19 hg, www.covid19hg.org/partners/. 2020.
  9. Ellinghaus, David , et “Genomewide Association Study of Severe Covid-19 with Respiratory Failure.” The NEW ENGLAND JOURNAL of MEDICINE, 17 June 2020, www.nejm.org/doi/10.1056/NEJMoa2020283.
  10. The COVID-19 Host Genetics “Results.” COVID-19 hg https://www.covid19hg.org/results/. 2020.
  11. Latz, Christopher A et “Blood type and outcomes in patients with COVID-19.” Annals of hematology, 1–6. 12 Jul. 2020, doi:10.1007/s00277-020-04169-1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354354/
  12. Menni, “Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection.” medRxiv 2020.04.05.20048421; doi: https://doi.org/10.1101/2020.04.05.20048421 https://www.medrxiv.org/content/10.1101/2020.04.05.20048421v1
  13. The COVID-19 Host Genetics “Phenotype definitions for analyses” COVID-19 hg, https://www.covid19hg.org/about/. Apr. 2020.
  14. The Michigan Imputation https://imputationserver.sph.umich.edu/index.html#!
  15. SAIGE https://github.com/weizhouUMICH/SAIGE
  16. The COVID-19 Host Genetics “In Silico Follow-up Results.” COVID-19 hg https://www.covid19hg.org/blog/2020-06-29-in-silico-follow-up-results/. 2020.
  17. Pathak, Gita , et al. Transcriptome-wide Association Study for COVID19 -HGI Freeze 3 Phenotypes Using S-PrediXcan. https://storage.googleapis.com/covid19-hg-in-silico-followup/PrediXcan/README-TWAS-COVID19-HGI-PathakG-June30_final.pdf. 30 June 2020.
  18. The RECOVERY Collaborative “Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report.” The NEW ENGLAND JOURNAL of MEDICINE, 17 July 2020, doi: 10.1056/NEJMoa2021436. https://www.nejm.org/doi/full/10.1056/NEJMoa2021436
  19. Schooling, “Genetic validation of the use of tocilizumab, statins and dexamethasone in COVID-19”. medRxiv 2020.07.09.20149450; doi: https://doi.org/10.1101/2020.07.09.20149450 https://www.medrxiv.org/content/10.1101/2020.07.09.20149450v1 )
  20. Zeberg, Hugo, and Svante Pääbo. “The major genetic risk factor for severe COVID-19 is inherited from ” bioRxiv , doi: doi.org/10.1101/2020.07.03.186296. 3 July 2020. https://www.biorxiv.org/content/10.1101/2020.07.03.186296v1.full
  21. Enard, David, and Dmitri Petrov. “Evidence that RNA Viruses Drove Adaptive Introgression between Neanderthals and Modern Humans.” Cell, vol. 175, no. 2, 4 Oct. 2018, doi:.org/10.1016/j.cell.2018.08.034. https://www.cell.com/cell/fulltext/S0092-8674(18)31095-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.%20com%2Fretrieve%2Fpii%2FS009286741831095X%3Fshowall%3Dtrue
  22. Sabeti, (2008) Natural selection: uncovering mechanisms of evolutionary adaptation to infectious disease. Nature Education 1(1):13 https://www.nature.com/scitable/topicpage/natural-selection-uncovering-mechanisms-of-evolutionary-adaptation-34539/
  23. www.genecards.org/cgi-bin/carddisp.pl?gene=SLC6A20. Accessed 29 July 2020.
  24. www.genecards.org/cgi-bin/carddisp.pl?gene=LZTFL1. Accessed 29 July 2020.
  25. www.genecards.org/cgi-bin/carddisp.pl?gene=CCR9. Accessed 29 July 2020.
  26. www.genecards.org/cgi-bin/carddisp.pl?gene=FYCO1. Accessed 29 July 2020.
  27. www.genecards.org/cgi-bin/carddisp.pl?gene=CXCR6. Accessed 29 July 2020.
  28. www.genecards.org/cgi-bin/carddisp.pl?gene=XCR1. Accessed 29 July 2020.