Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health...

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Bibliographic Details
Published in:	PloS one 2021-01, Vol.16 (1), p.e0238665-e0238665
Main Authors:	Oulas, Anastasis, Zanti, Maria, Tomazou, Marios, Zachariou, Margarita, Minadakis, George, Bourdakou, Marilena M, Pavlidis, Pavlos, Spyrou, George M
Format:	Article
Language:	eng
Subjects:	Bioinformatics Biology and Life Sciences Coronavirus Nucleocapsid Proteins - chemistry Coronavirus Nucleocapsid Proteins - genetics Coronaviruses COVID-19 COVID-19 - transmission COVID-19 - virology Data analysis Editing Gene mutations Generalized linear models Genetics Geographic Information Systems Health aspects Humans Linear Models Measurement Medicine Medicine and health sciences Methodology Middle East respiratory syndrome Mutation Neurology Pandemics Phosphoproteins - chemistry Phosphoproteins - genetics Phylogeny Polymorphism, Single Nucleotide Response time SARS-CoV-2 - classification SARS-CoV-2 - genetics SARS-CoV-2 - pathogenicity Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Statistical models Viral diseases Viroporin Proteins - chemistry Viroporin Proteins - genetics Virulence Virulence (Microbiology) Viruses
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Summary:	This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.
ISSN:	1932-6203 1932-6203