Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains.
PLoS One
; 16(1): e0238665, 2021.
Article
in English
| MEDLINE | ID: covidwho-1048815
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Viroporin Proteins
/
Coronavirus Nucleocapsid Proteins
/
SARS-CoV-2
/
COVID-19
Type of study:
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pone.0238665
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