Open AccessGeneralized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains
Author(s) -
Anastasis Oulas,
Maria Zanti,
Marios Tomazou,
Margarita Zachariou,
George Minadakis,
Marilena M. Bourdakou,
Margarita Zachariou,
George M. Spyrou
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0238665
Subject(s) - virulence , mutation , genetics , biology , mutation rate , covid-19 , gene , virology , computational biology , medicine , disease , pathology , infectious disease (medical specialty)
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.