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Will GWAS eventually allow the identification of genomic biomarkers for COVID-19 severity and mortality?
Vito Luigi Colona,1 Michela Biancolella,2 Antonio Novelli,3 and Giuseppe Novelli1,4,5
1Department of Biomedicine and Prevention and
2Department of Biology, Tor Vergata University of Rome, Rome, Italy.
3Laboratory of Medical Genetics, IRCCS Bambino Gesù Children’s Hospital, Rome, Italy.
4IRCCS Neuromed, Pozzilli (IS), Italy.
5Department of Pharmacology, School of Medicine, University of Nevada, Reno, Nevada, USA.
Address correspondence to: Giuseppe Novelli, Viale Oxford, 81 – 00133 Rome (RM), Italy. Phone: 39.06.20900670; Email: novelli@med.uniroma2.it.
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1Department of Biomedicine and Prevention and
2Department of Biology, Tor Vergata University of Rome, Rome, Italy.
3Laboratory of Medical Genetics, IRCCS Bambino Gesù Children’s Hospital, Rome, Italy.
4IRCCS Neuromed, Pozzilli (IS), Italy.
5Department of Pharmacology, School of Medicine, University of Nevada, Reno, Nevada, USA.
Address correspondence to: Giuseppe Novelli, Viale Oxford, 81 – 00133 Rome (RM), Italy. Phone: 39.06.20900670; Email: novelli@med.uniroma2.it.
Find articles by Biancolella, M. in: JCI | PubMed | Google Scholar
1Department of Biomedicine and Prevention and
2Department of Biology, Tor Vergata University of Rome, Rome, Italy.
3Laboratory of Medical Genetics, IRCCS Bambino Gesù Children’s Hospital, Rome, Italy.
4IRCCS Neuromed, Pozzilli (IS), Italy.
5Department of Pharmacology, School of Medicine, University of Nevada, Reno, Nevada, USA.
Address correspondence to: Giuseppe Novelli, Viale Oxford, 81 – 00133 Rome (RM), Italy. Phone: 39.06.20900670; Email: novelli@med.uniroma2.it.
Find articles by Novelli, A. in: JCI | PubMed | Google Scholar
1Department of Biomedicine and Prevention and
2Department of Biology, Tor Vergata University of Rome, Rome, Italy.
3Laboratory of Medical Genetics, IRCCS Bambino Gesù Children’s Hospital, Rome, Italy.
4IRCCS Neuromed, Pozzilli (IS), Italy.
5Department of Pharmacology, School of Medicine, University of Nevada, Reno, Nevada, USA.
Address correspondence to: Giuseppe Novelli, Viale Oxford, 81 – 00133 Rome (RM), Italy. Phone: 39.06.20900670; Email: novelli@med.uniroma2.it.
Find articles by
Novelli, G.
in:
JCI
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PubMed
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Google Scholar
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Published October 21, 2021 - More info
J Clin Invest. 2021;131(23):e155011. https://doi.org/10.1172/JCI155011.
© 2021 American Society for Clinical Investigation
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Abstract
Background There is considerable variability in COVID-19 outcomes among younger adults, and some of this variation may be due to genetic predisposition.Methods We combined individual level data from 13,888 COVID-19 patients (n = 7185 hospitalized) from 17 cohorts in 9 countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. We next performed metaanalyses using FinnGen and the Columbia University COVID-19 Biobank.Results We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (HR, 1.4; 95% CI, 1.2–1.7). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (OR, 2.1; 95% CI, 1.6–2.6), venous thromboembolism (OR, 1.7; 95% CI, 1.2–2.4), and hepatic injury (OR, 1.5; 95% CI, 1.2–2.0). Risk allele carriers age 60 years and younger had higher odds of death or severe respiratory failure (OR, 2.7; 95% CI, 1.8–3.9) compared with those of more than 60 years (OR, 1.5; 95% CI, 1.2–1.8; interaction, P = 0.038). Among individuals 60 years and younger who died or experienced severe respiratory failure, 32.3% were risk-variant carriers compared with 13.9% of those not experiencing these outcomes. This risk variant improved the prediction of death or severe respiratory failure similarly to, or better than, most established clinical risk factors.Conclusions The major common COVID-19 genetic risk factor is associated with increased risks of morbidity and mortality, which are more pronounced among individuals 60 years or younger. The effect was similar in magnitude and more common than most established clinical risk factors, suggesting potential implications for future clinical risk management.
Authors
Tomoko Nakanishi, Sara Pigazzini, Frauke Degenhardt, Mattia Cordioli, Guillaume Butler-Laporte, Douglas Maya-Miles, Luis Bujanda, Youssef Bouysran, Mari E.K. Niemi, Adriana Palom, David Ellinghaus, Atlas Khan, Manuel Martínez-Bueno, Selina Rolker, Sara Amitrano, Luisa Roade Tato, Francesca Fava, FinnGen, The COVID-19 Host Genetics Initiative (HGI), Christoph D. Spinner, Daniele Prati, David Bernardo, Federico Garcia, Gilles Darcis, Israel Fernández-Cadenas, Jan Cato Holter, Jesus M. Banales, Robert Frithiof, Krzysztof Kiryluk, Stefano Duga, Rosanna Asselta, Alexandre C. Pereira, Manuel Romero-Gómez, Beatriz Nafría-Jiménez, Johannes R. Hov, Isabelle Migeotte, Alessandra Renieri, Anna M. Planas, Kerstin U. Ludwig, Maria Buti, Souad Rahmouni, Marta E. Alarcón-Riquelme, Eva C. Schulte, Andre Franke, Tom H. Karlsen, Luca Valenti, Hugo Zeberg, J. Brent Richards, Andrea Ganna
Prediction is very difficult, especially when it comes to the future.
–Niels Bohr
Numerous research groups around the world have begun to analyze different genomes of COVID-19 patients in order to identify susceptibility loci to SARS-CoV-2 and alleles correlated with the severity of the disease (1–6). Since the beginning of the pandemic, GWAS have represented an indispensable approach to establishing molecular factors and have shed light on the pathological mechanisms of COVID-19.
COVID-19 is a complex disorder in which both genetic and environmental factors, modifiable or not, synergistically influence disease outcome (5, 6). Although the exponential growth of genomic data has made parallel data sharing and analysis possible during a short time (7), many of the biobanks currently available lack essential complementary patient information, such as ethnic origin, pathological course, pharmacological treatment, and the results of the therapy itself. The collection of samples, for both cases and controls, are influenced by many variables, including sociodemographic factors, which can lead to selection bias (8). Computer algorithms applied to extended genomic studies can lead to interpretive errors if they are based on representative alleles from international reference databases and certain predictive functional bioinformatics characteristics (9, 10). Nonetheless, these predictions represent a valid starting point for studying the impact of different gene loci and are essential for a more in-depth understanding of the cellular pathways involved in the virus-host interaction (5, 6). For example, researchers have recently identified two genomic regions that encompass genes involved in type I IFN antiviral immunity, which are considered the principal targets of germinal mutations of inborn errors of immunity (IEIs) (11). In fact, IEIs compromise type I IFN immunity, either directly or via autoantibodies, favoring the development of potentially lethal forms of COVID-19. In general, the impact of susceptibility alleles identified by GWAS appears to be modest at the individual level, but they can operate as modifiers and, if analyzed as a whole, could reach appreciable ORs. For this reason, genomic analysis is necessary to establish polygenic risk scores used for patient stratification and targeted treatment.
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Benefits of integrated GWAS
GWAS will certainly continue to play a key role in the identification of genes for understanding virus-host interaction. The identification of susceptibility loci represents a fundamental step in scientific research and lays the foundations for the identifying targets relevant for the development of therapeutic approaches and/or the repositioning of drugs in use or disused. The preparation of a polygenic risk score, which can be integrated with the so-called phenome, must be considered as a starting point in continuous evolution, drawing on data from PheWAS.
The complementation of GWAS and PheWAS and the validation of the results obtained through functional studies must be considered as the key objective in a proactive perspective of precision medicine (Figure 1). Achieving this milestone will take time and effort, but it is the only way to integrate genomics into clinical practice.
Figure 1A model for precision medicine. In a proactive approach to precision medicine, individual health determinants are systematically complemented with GWAS, PheWAS, and functional studies. Nakanishi et al. found that the common COVID-19 genetic risk factor (rs10490770) associates with increased risks of morbidity and mortality, especially in patients under 60 years (12). Efforts to establish polygenic risk scores, validate candidate genes with mechanistic understanding, and determine additional risk factors will help clinicians stratify patients to provide targeted treatments OSAS, obstructive sleep apnea syndrome.
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