RT Journal Article
SR Electronic
T1 Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.17.253484
DO 10.1101/2020.08.17.253484
A1 Oulas, Anastasis
A1 Zanti, Maria
A1 Tomazou, Marios
A1 Zachariou, Margarita
A1 Minadakis, George
A1 Bourdakou, Marilena M
A1 Pavlidis, Pavlos
A1 Spyrou, George M.
YR 2020
UL http://biorxiv.org/content/early/2020/08/18/2020.08.17.253484.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.