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SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk

Korves et al., medRxiv, doi:10.1101/2024.03.08.24303818
Mar 2024  
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Retrospective 12,538 COVID-19 patients, showing associations between specific SARS-CoV-2 lineages and amino acid mutations and increased hospitalization risk, while infection with omicron was associated with lower hospitalization risk compared to prior variants. The study used machine learning (XGBoost) and hierarchical Bayesian modeling to analyze relationships between viral genomic features and hospitalization within 14 days, while accounting for patient risk factors, COVID-19 vaccination status, and monoclonal antibody treatment. Several lineages including B.1.1.7, AY.44, and AY.54 were associated with higher hospitalization risk. Amino acid changes in the spike protein N-terminal domain and in non-structural protein 14 were also associated with hospitalization risk.
Korves et al., 10 Mar 2024, USA, preprint, 5 authors. Contact: sroberts@mitre.org.
This PaperMiscellaneousAll
SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk
Tonia Korves, David Stein, David Walburger, Tomasz Adamusiak, Seth Roberts
doi:10.1101/2024.03.08.24303818
Variants of SARS-CoV-2 have been associated with different transmissibilities and disease severities. The present study examines SARS-CoV-2 genetic variants and their relationship to risk for hospitalization, using data from 12,538 patients from a large, multisite observational cohort study. The association of viral genomic variants and hospitalization is examined with clinical covariates, including COVID-19 vaccination status, outpatient monoclonal antibody treatment status, and underlying risk for poor clinical outcome. Modeling approaches include XGBoost with SHapley Additive exPlanations (SHAP) analysis and generalized linear mixed models. The results indicate that several SARS-CoV-2 lineages are associated with increased hospitalization risk, including B.1.1.7, AY.44, and AY.54. As found in prior studies, Omicron is associated with lower hospitalization risk compared to prior WHO variants. In addition, the results suggest that variants at specific amino acid locations, including locations within Spike protein N-terminal domain and in non-structural protein 14, are associated with hospitalization risk.
Competing Interests There are no competing interests to declare.
References
Agarwal, Leblond, Mcauley, Linking genotype to phenotype: Further exploration of mutations in SARS-CoV-2 associated with mild or severe outcomes, Internet, doi:10.1101/2022.04.15.22273922v1
Aiewsakun, Nilplub, Wongtrakoongate, SARS-CoV-2 genetic variations associated with COVID-19 pathogenicity [Internet], Microbial Genomics, doi:10.1099/mgen.0.000734
Aksamentov, Roemer, Hodcroft, Nextclade: Clade assignment, mutation calling and quality control for viral genomes [Internet, Journal of Open Source Software, doi:10.21105/joss.03773
Ambrose, Amin, Anderson, Descriptive analysis of SARS-CoV-2 genomics data from ambulatory patients, Internet, doi:10.1101/2023.05.03.23289106v1
Ambrose, Amin, Anderson, Neutralizing monoclonal antibody use and COVID-19 infection outcomes [Internet, JAMA Network Open
Berman, Westbrook, Feng, The protein data bank, Nucleic Acids Research
Bingham, Chen, Jankowiak, Pyro: Deep universal probabilistic programming [Internet, Journal of Machine Learning Research
Carabelli, Peacock, Thorne, SARS-CoV-2 variant biology: Immune escape, transmission and fitness [Internet], Nature reviews Microbiology
Chen, Guestrin, XGBoost: A scalable tree boosting system
Cingolani, Platts, Wang, A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of drosophila melanogaster strain w1118; iso-2; iso-3 [Internet, Fly, doi:10.4161/fly.19695
Elixhauser, Steiner, Harris, Comorbidity measures for use with administrative data [Internet], Medical care
Esper, Cheng, Adhikari, Genomic epidemiology of SARS-CoV-2 infection during the initial pandemic wave and association with disease severity [Internet, JAMA network open
Goyal, Bruyne, Belkum A Van, Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients, Infection, Genetics and Evolution
Grubaugh, Gangavarapu, Quick, An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar, bioRxiv, doi:10.1101/383513v1
Hahn, Wu, Lee, Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with p.1 of the Brazilian strain [Internet], Genetic Epidemiology, doi:10.1002/gepi.22421
Hsu, Laurent-Rolle, Pawlak, Translational shutdown and evasion of the innate immune response by SARS-CoV-2 NSP14 protein [Internet, Proceedings of the National Academy of Sciences of the United States of America, doi:10.1073/pnas.2101161118
Johnsen, Riemer-Sørensen, Dewan, A new method for exploring genegene and gene-environment interactions in GWAS with tree ensemble methods and SHAP © 2024 The MITRE Corporation, All Rights Reserved Approved for Public Release. Distribution Unlimited, doi:10.1186/s12859-021-04041-7
Johnsen, Strümke, Langaas, Inferring feature importance with uncertainties with application to large genotype data [Internet], PLOS Computational Biology, doi:10.1371/journal.pcbi.1010963
Jumper, Evans, Pritzel, Highly accurate protein structure prediction with AlphaFold [Internet], Nature
Khare, Gurry, Freitas, GISAID's role in pandemic response [Internet], China CDC Weekly
Kind, Buckingham, Making neighborhood-disadvantage metrics accessible -the neighborhood atlas, New England Journal of Medicine
Koch, Du, Dressner, Demographic and viral-genetic analyses of COVID-19 severity in Bahrain identify local risk factors and a protective effect of polymerase mutations
Krivov, Shapovalov, Dunbrack, Improved prediction of protein side-chain conformations with SCWRL4 [Internet], Proteins: Structure, Function, and Bioinformatics, doi:10.1002/prot.22488
Liang, Ding, Liu, Identification of critical SARS-CoV-2 amino acids associated with COVID-19 hospitalization rate using machine learning and statistical modeling: An observational study in the United States [Internet]. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Lundberg, Allen, Lee, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems
Maher, Bartha, Weaver, Predicting the mutational drivers of future SARS-CoV-2 variants of concern [Internet], Science translational medicine
Maurya, Mishra, Swaminathan, SARS-CoV-2 mutations and COVID-19 clinical outcome: Mutation global frequency dynamics and structural modulation hold the key [Internet], Frontiers in cellular and infection microbiology
Mayer, SHAP visualizations [r package shapviz version 0
Mehta, Alle, Chaturvedi, Clinico-genomic analysis reveals mutations associated with COVID-19 disease severity: Possible modulation by RNA structure [Internet, Pathogens
Meng, Goddard, Pettersen, UCSF ChimeraX: Tools for structure building and analysis [Internet], Protein Science, doi:10.1002/pro.4792
Nagy, Pongor, Győrffy, Different mutations in SARS-CoV-2 associate with severe and mild outcome [Internet, International journal of antimicrobial agents
O'toole, Scher, Underwood, Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool [Internet, Virus Evolution
Obermeyer, Jankowiak, Barkas, Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness [Internet], Science, doi:10.1126/science.abm1208
Ramarao-Milne, Jain, Sng, Data-driven platform for identifying variants of interest in COVID-19 virus [Internet], Computational and structural biotechnology journal
Rouder, Speckman, Sun, Bayesian t tests for accepting and rejecting the null hypothesis [Internet], Psychonomic Bulletin and Review, doi:10.3758/PBR.16.2.225
Shen, Triche, Bard, Spike protein NTD mutation G142D in SARS-CoV-2 delta VOC lineages is associated with frequent back mutations, increased viral loads, and immune evasion, Internet, doi:10.1101/2021.09.12.21263475v1
Sokhansanj, Zhao, Rosen, An interpretable deep learning model for predicting the risk of severe COVID-19 from spike protein sequence
Turakhia, Thornlow, Hinrichs, Ultrafast sample placement on existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic [Internet], Nature Genetics
Verdinelli, Wasserman, Computing bayes factors using a generalization of the savage-dickey density ratio, Journal of the American Statistical Association
Voss, Skarzynski, Mcauley, Variants in SARS-CoV-2 associated with mild or severe outcome [Internet], Evolution, Medicine, and Public Health
Wang, Chen, Hozumi, Decoding asymptomatic COVID-19 infection and transmission [Internet], Journal of Physical Chemistry Letters, doi:10.1021/acs.jpclett.0c02765
Wingate, Weber, Automated variational inference in probabilistic programming
Wu, Zhao, Yu, A new coronavirus associated with human respiratory disease in china [Internet], Nature
Yan, Machine learning evaluation metrics, r package MLmetrics version
Zhang, Tan, Ling, Viral and host factors related to the clinical outcome of COVID-19 [Internet], Nature
Zhu, Marsh, Griffith, Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-may 2021, PloS one
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Frontiers in cellular and ' 'infection microbiology 2022; 12 Available from: ' 'https://pubmed.ncbi.nlm.nih.gov/35386683/', 'DOI': '10.3389/fcimb.2022.868414'}, { 'key': '2024031300400911000_2024.03.08.24303818v1.11', 'doi-asserted-by': 'crossref', 'first-page': '104730', 'DOI': '10.1016/j.meegid.2021.104730', 'article-title': 'Different SARS-CoV-2 haplotypes associate with geographic origin and ' 'case fatality rates of COVID-19 patients', 'volume': '90', 'year': '2021', 'journal-title': 'Infection, Genetics and Evolution'}, { 'key': '2024031300400911000_2024.03.08.24303818v1.12', 'doi-asserted-by': 'publisher', 'DOI': '10.1038/s41586-020-2355-0'}, { 'key': '2024031300400911000_2024.03.08.24303818v1.13', 'doi-asserted-by': 'crossref', 'unstructured': 'Mehta P , Alle S , Chaturvedi A , et al.: Clinico-genomic analysis ' 'reveals mutations associated with COVID-19 disease severity: Possible ' 'modulation by RNA structure [Internet]. 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