A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death (2024)

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death / F. Minnai, F. Biscarini, M. Esposito, T.A. Dragani, L. Bujanda, S. Rahmouni, M.E. Alarcón-Riquelme, D. Bernardo, E. Carnero-Montoro, M. Buti, H. Zeberg, R. Asselta, M. Romero-Gómez, F. Mari, S. Daga, I. Meloni, G. Brunelli, M. Lista, D. Maffeo, E. Pasquinelli, E. Antolini, S.L. Basso, S. Minetto, G. Rollo, A. Rina, M. Rozza, R. Tita, M.A. Mencarelli, C.L. Rizzo, A.M. Pinto, F. Ariani, F. Montagnani, M. Tumbarello, I. Rancan, M. Fabbiani, P. Cameli, D. Bennett, F. Anedda, S. Marcantonio, S. Scolletta, F. Franchi, M.A. Mazzei, S. Guerrini, E. Conticini, L. Cantarini, B. Frediani, D. Tacconi, C.S. Raffaelli, A. Emiliozzi, M. Feri, A. Donati, R. Scala, L. Guidelli, G. Spargi, M. Corridi, C. Nencioni, L. Croci, G.P. Caldarelli, D. Romani, P. Piacentini, M. Bandini, E. Desanctis, S. Cappelli, A. Canaccini, A. Verzuri, V. Anemoli, A. Ognibene, M. Lorubbio, A. Pancrazzi, M. Vaghi, A.D.'. Monforte, F.G. Miraglia, M.U. Mondelli, S. Mantovani, R. Bruno, M. Vecchia, M. Maffezzoni, E. Martinelli, M. Girardis, S. Busani, S. Venturelli, A. Cossarizza, A. Antinori, A. Vergori, S. Rusconi, M. Siano, A. Gabrieli, A. Riva, D. Francisci, E. Schiaroli, C. Pallotto, S.G. Parisi, M. Basso, S. Panese, S. Baratti, P.G. Scotton, F. Andretta, M. Giobbia, R. Scaggiante, F. Gatti, F. Castelli, E. Quiros-Roldan, M.D. Antoni, I. Zanella, M.D. Monica, C. Piscopo, M. Capasso, R. Russo, I. Andolfo, A. Iolascon, G. Fiorentino, M. Carella, M. Castori, G. Merla, G.M. Squeo, F. Aucella, P. Raggi, R. Perna, M. Bassetti, A. Di Biagio, M. Sanguinetti, L. Masucci, A. Guarnaccia, S. Valente, A. Di Florio, M. Mandalà, A. Giorli, L. Salerni, P. Zucchi, P. Parravicini, E. Menatti, T. Trotta, F. Giannattasio, G. Coiro, G. Lacerenza, C. Mussini, L. Tavecchia, L. Crotti, G. Parati, R. Menè, M. Sanarico, M. Gori, F. Raimondi, A. Stella, F. Biscarini, T. Bachetti, M.T. La Rovere, M. Bussotti, S. Ludovisi, K. Capitani, S. Dei, S. Ravaglia, A. Giliberti, G. Gori, R. Artuso, E. Andreucci, A. Perrella, F. Bianchi, P. Bergomi, E. Catena, R. Colombo, S. Luchi, G. Morelli, P. Petrocelli, S. Iacopini, S. Modica, S. Baroni, G. Micheli, M. Falcone, D. Urso, G. Tiseo, T. Matucci, A. Pulcinelli, D. Grassi, C. Ferri, F. Marinangeli, F. Brancati, A. Vincenti, V. Borgo, S. Lombardi, M. Lenzi, M.A. Di Pietro, L. Attala, C. Costa, A. Gabbuti, A. Bellucci, M. Colaneri, P. Casprini, C. Pomara, M. Esposito, R. Leoncini, M. Cirianni, L. Galasso, M.A. Bellini, C. Gabbi, N. Picchiotti, S. Furini, E. Pelo, B. Minuti, F. Gerundino, C. Lazzeri, A. Vecchi, L. Bianchi, E. Venturini, C. Montagnani, E. Chiappini, C. Beltrami, L. Galli, I. Fernandez-Cadenas, C. Fallerini, K. Zguro, S. Croci, M. Baldassarri, M. Bruttini, S. Furini, A. Renieri, F. Colombo. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 14:1(2024 Feb 06), pp. 3000.1-3000.14. [10.1038/s41598-024-53310-x]

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

F. Minnai;R. Asselta;A.D.'. Monforte;S. Rusconi;A. Gabrieli;A. Riva;F. Bianchi;M. Colaneri;
2024

Abstract

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.

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Settore MED/17 - Malattie Infettive

6-feb-2024

SCIENTIFIC REPORTS

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A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death (2024)

FAQs

What is a genome-wide association study quizlet? ›

A genome-wide association study is an approach that involves rapidly scanning markers across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular disease.

What is genome-wide association study? ›

A genome-wide association study (GWAS) is an approach to compare the genomes from many different people to find genetic markers associated with a particular phenotype or risk of disease.

What kind of diseases are studied using genome-wide association studies? ›

Genome-wide association studies have been a powerful approach to the identification of genetic susceptibility loci in complex genetic disorders such as rheumatoid arthritis, inflammatory bowel disease, and coronary artery disease [135–138].

Are genome-wide association studies used to identify potential genetic variants? ›

Genome-wide association studies

GWAS is an approach used in genetics research to associate specific genetic variants with a phenotype (e.g., diseases, traits, behavioral outcomes) and scan the genomes from many different people and examine these associations.

Which type of variant is studied in genome-wide association studies? ›

GWAS can consider copy-number variants or sequence variations in the human genome, although the most commonly studied genetic variants in GWAS are single-nucleotide polymorphisms (SNPs).

What is a characteristic of a genome-wide association study? ›

​Genome-Wide Association Studies (GWAS)

The method involves surveying the genomes of many people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait.

What are the disadvantages of genome-wide association studies? ›

GWAS cannot identify all genetic determinants of complex traits. It is unlikely that GWAS will ever explain 100% of the heritability of complex traits. This limitation is not exclusive to GWAS, as no method or technology to date can identify all the genetic components of complex traits.

What does GWAS data look like? ›

GWAS results are often displayed in a Manhattan plot (Figure 3) with -log10 (p-value) plotted against the position in the genome. The GWAS Catalog is a structured repository which provides summary data from all published human GWAS studies, in a consistent, searchable format.

What is the difference between GWAS and sequencing? ›

GWAS is the association of individual markers or groups of markers across the genome with phenotypic data. Markers are allele calls of a representative set of loci across the genome. Whole genome sequencing is an assay that is literally what it says it is. It gets sequence data, not markers.

What is the problem with GWAS studies? ›

A GWAS scans across the genome for loci that are associated with a phenotype. However, GWASs are susceptible to confounding due to gene–environment correlation (environmental confounding) and correlations with other genetic variants across the genome (genetic confounding).

What is the controversy with GWAS? ›

A central point of debate on GWA studies has been that most of the SNP variations found by GWA studies are associated with only a small increased risk of the disease, and have only a small predictive value. The median odds ratio is 1.33 per risk-SNP, with only a few showing odds ratios above 3.0.

What have we learned from genome-wide association studies in psychiatry? ›

GWAS has provided real data about the genetic basis of psychiatric disorders. Genetic architecture refers to the number of loci conferring risk for a disorder and their frequencies, effect sizes, modes of action, and interactions with other genetic loci and environmental factors.

What are genome-wide association studies used to study? ›

Genome-wide association studies (GWAS) help scientists identify genes associated with a particular disease (or another trait). This method studies the entire set of DNA (the genome) of a large group of people, searching for small variations, called single nucleotide polymorphisms or SNPs (pronounced “snips”).

How accurate is GWAS? ›

In contrast to the candidate gene and linkage study era before 2007, where many findings in common disease genetics proved to be false positives, the vast majority of associations identified by GWASs are extremely robust statistically and are reproducible in additional studies.

What is genome-wide association studies in bacteria? ›

Microbial genome-wide association studies (mGWAS) are a new area of research aimed at identifying genetic variants in microbial genomes that are associated with host variation in or microbe phenotypes, for example genetic variation affecting phenotypes such as carriage (Lees et al., 2017) in humans and virulence ( ...

What are genome-wide association studies summary statistics? ›

Summary statistics are defined as the aggregate p-values and association data for every variant analysed in a genome-wide association study (GWAS). The GWAS Catalog maintains a standard format for summary statistics, to ensure that datasets from a wide range of sources are as interoperable as possible.

What are genome-wide association studies in Alzheimer's? ›

Variants discovered by genome-wide associations are mostly common to low-frequency with small effect sizes. To date, AD/dementia GWAS have identified 101 independent AD-associated single nucleotide polymorphisms across 81 genome-wide significant (p < 5e-8) loci.

What is the difference between genome-wide association studies and candidate gene association studies? ›

The key distinction between the genome-wide versus candidate gene studies is that the former make no a priori assumptions regarding which genes may be involved and thereby allow for the discovery of potentially novel genes.

What is the study of genome-wide gene expression? ›

Genome-wide analysis of gene expression is the study of transcription at a genomic scale, also known as transcriptomics.

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