ArticleMicrobial genomics2021
SARS-CoV-2 genetic variations associated with COVID-19 pathogenicity.
Article in Microbial genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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Who cites it
23 citing papers in PubMed, 40 citations in OpenAlex.
- Translational insights into Long-COVID: evaluation of preclinical animal models along the lung-brain-immune axis with focus on Golden Syrian Hamsters.Journal of neuroinflammation · 2026Review
- Impact of SARS-CoV-2 Variant NSP6 on Pathogenicity: Genetic Analysis and Cell Biology.Current issues in molecular biology · 2025Article
- Contribution of parasite and host genotype to immunopathology of schistosome infections.Parasites & vectors · 2024Article
- Contribution of parasite and host genotype to immunopathology of schistosome infections.bioRxiv : the preprint server for biology · 2024Article
- Enhanced detection and molecular modeling of adaptive mutations in SARS-CoV-2 coding and non-coding regions using the c/µ test.Virus evolution · 2024Article
- The multiple roles of nsp6 in the molecular pathogenesis of SARS-CoV-2.Antiviral research · 2023Review
- SARS-CoV-2 Variants by Whole-Genome Sequencing in a University Hospital in Bangkok: First to Third COVID-19 Waves.Pathogens (Basel, Switzerland) · 2023Article
- Article
- Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.Computers in biology and medicine · 2022Article
- Article
- Advances in the development of therapeutic strategies against COVID-19 and perspectives in the drug design for emerging SARS-CoV-2 variants.Computational and structural biotechnology journal · 2022Review
- Genomic Analysis of AZD1222 (ChAdOx1) Vaccine Breakthrough Infections in the City of Mumbai.International journal of clinical practice · 2022Observational
- Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-May 2021.PloS one · 2022Article
- Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.Current genomics · 2021Article
- SARS-CoV-2 genetic variations associated with COVID-19 pathogenicity.Microbial genomics · 2021Article
- Article
- SARS-CoV-2 Infectivity and Severity of COVID-19 According to SARS-CoV-2 Variants: Current Evidence.Journal of clinical medicine · 2021Review
- Role of SARS-CoV-2 and ACE2 variations in COVID-19.Biomedical journal · 2021Review
- Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa.medRxiv : the preprint server for health sciences · 2021Article
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we performed genome-wide association analyses on SARS-CoV-2 genomes to identify genetic mutations associated with pre-symptomatic/asymptomatic COVID-19 cases. Various potential covariates and confounding factors of COVID-19 severity, including patient age, gender and country, as well as virus phylogenetic relatedness were adjusted for. In total, 3021 full-length genomes of SARS-CoV-2 generated from original clinical samples and whose patient status could be determined conclusively as either 'pre-symptomatic/asymptomatic' or 'symptomatic' were retrieved from the GISAID database. We found that the mutation 11 083G>T, located in the coding region of non-structural protein 6, is significantly associated with asymptomatic COVID-19. Patient age is positively correlated with symptomatic infection, while gender is not significantly correlated with the development of the disease. We also found that the effects of the mutation, patient age and gender do not vary significantly among countries, although each country appears to have varying baseline chances of COVID-19 symptom development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.