ORCID Profile
0000-0002-7725-4813
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Publisher: Public Library of Science (PLoS)
Date: 11-08-2021
DOI: 10.1371/JOURNAL.PONE.0255402
Abstract: Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7 rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.
Publisher: SAGE Publications
Date: 2015
DOI: 10.4137/CIN.S19965
Abstract: We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation, and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modeling paradigms, two ex le applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second ex le is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue.
Publisher: Cold Spring Harbor Laboratory
Date: 20-07-2022
DOI: 10.1101/2022.07.19.500636
Abstract: Due to postmortem DNA degradation, most ancient genomes sequenced to date have low depth of coverage, preventing the true underlying genotypes from being recovered. Genotype imputation has been put forward to improve genotyping accuracy for low-coverage genomes. However, it is unknown to what extent imputation of ancient genomes produces accurate genotypes and whether imputation introduces bias to downstream analyses. To address these questions, we downs led 43 ancient genomes, 42 of which are high-coverage (above 10x) and three constitute a trio (mother, father and son), from different times and continents to simulate data with coverage in the range of 0.1x-2.0x and imputed these using state-of-the-art methods and reference panels. We assessed imputation accuracy across ancestries and depths of coverage. We found that ancient and modern DNA imputation accuracies were comparable. We imputed most of the 42 high-coverage genomes downs led to 1x with low error rates (below 5%) and estimated higher error rates for African genomes, which are underrepresented in the reference panel. We used the ancient trio data to validate imputation and phasing results using an orthogonal approach based on Mendel’s rules of inheritance. This resulted in imputation and switch error rates of 1.9% and 2.0%, respectively, for 1x genomes. We further compared the results of downstream analyses between imputed and high-coverage genomes, notably principal component analysis (PCA), genetic clustering, and runs of homozygosity (ROH). For these three approaches, we observed similar results between imputed and high-coverage genomes using depths of coverage of at least 0.5x, except for African genomes, for which the decreased imputation accuracy impacted ROH estimates. Altogether, these results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method with potential to expand and improve ancient DNA studies.
Publisher: Springer Science and Business Media LLC
Date: 20-06-2023
Publisher: Springer Science and Business Media LLC
Date: 08-07-2021
DOI: 10.1038/S41586-021-03767-X
Abstract: The genetic make-up of an in idual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Simone Rubinacci.