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Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness
Repeated emergence of SARS-CoV-2 variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR(0), a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geograp...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161372/ https://www.ncbi.nlm.nih.gov/pubmed/35608456 http://dx.doi.org/10.1126/science.abm1208 |
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author | Obermeyer, Fritz Jankowiak, Martin Barkas, Nikolaos Schaffner, Stephen F. Pyle, Jesse D. Yurkovetskiy, Leonid Bosso, Matteo Park, Daniel J. Babadi, Mehrtash MacInnis, Bronwyn L. Luban, Jeremy Sabeti, Pardis C. Lemieux, Jacob E. |
author_facet | Obermeyer, Fritz Jankowiak, Martin Barkas, Nikolaos Schaffner, Stephen F. Pyle, Jesse D. Yurkovetskiy, Leonid Bosso, Matteo Park, Daniel J. Babadi, Mehrtash MacInnis, Bronwyn L. Luban, Jeremy Sabeti, Pardis C. Lemieux, Jacob E. |
author_sort | Obermeyer, Fritz |
collection | PubMed |
description | Repeated emergence of SARS-CoV-2 variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR(0), a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR(0) to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR(0) forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization. |
format | Online Article Text |
id | pubmed-9161372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91613722022-06-06 Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness Obermeyer, Fritz Jankowiak, Martin Barkas, Nikolaos Schaffner, Stephen F. Pyle, Jesse D. Yurkovetskiy, Leonid Bosso, Matteo Park, Daniel J. Babadi, Mehrtash MacInnis, Bronwyn L. Luban, Jeremy Sabeti, Pardis C. Lemieux, Jacob E. Science Reports Repeated emergence of SARS-CoV-2 variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR(0), a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR(0) to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR(0) forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization. American Association for the Advancement of Science 2022-05-24 /pmc/articles/PMC9161372/ /pubmed/35608456 http://dx.doi.org/10.1126/science.abm1208 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Reports Obermeyer, Fritz Jankowiak, Martin Barkas, Nikolaos Schaffner, Stephen F. Pyle, Jesse D. Yurkovetskiy, Leonid Bosso, Matteo Park, Daniel J. Babadi, Mehrtash MacInnis, Bronwyn L. Luban, Jeremy Sabeti, Pardis C. Lemieux, Jacob E. Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title | Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title_full | Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title_fullStr | Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title_full_unstemmed | Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title_short | Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness |
title_sort | analysis of 6.4 million sars-cov-2 genomes identifies mutations associated with fitness |
topic | Reports |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161372/ https://www.ncbi.nlm.nih.gov/pubmed/35608456 http://dx.doi.org/10.1126/science.abm1208 |
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