Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection.
The global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance da...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-12-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1010540 |
| _version_ | 1797973925752209408 |
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| author | Martin Jankowiak Fritz H Obermeyer Jacob E Lemieux |
| author_facet | Martin Jankowiak Fritz H Obermeyer Jacob E Lemieux |
| author_sort | Martin Jankowiak |
| collection | DOAJ |
| description | The global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance data make rigorous analysis challenging. To meet this challenge, we develop Bayesian Viral Allele Selection (BVAS), a principled and scalable probabilistic method for inferring the genetic determinants of differential viral fitness and the relative growth rates of viral lineages, including newly emergent lineages. After demonstrating the accuracy and efficacy of our method through simulation, we apply BVAS to 6.9 million SARS-CoV-2 genomes. We identify numerous mutations that increase fitness, including previously identified mutations in the SARS-CoV-2 Spike and Nucleocapsid proteins, as well as mutations in non-structural proteins whose contribution to fitness is less well characterized. In addition, we extend our baseline model to identify mutations whose fitness exhibits strong dependence on vaccination status as well as pairwise interaction effects, i.e. epistasis. Strikingly, both these analyses point to the pivotal role played by the N501 residue in the Spike protein. Our method, which couples Bayesian variable selection with a diffusion approximation in allele frequency space, lays a foundation for identifying fitness-associated mutations under the assumption that most alleles are neutral. |
| first_indexed | 2024-04-11T04:11:56Z |
| format | Article |
| id | doaj.art-7c477a63c39542afb73ddfed9a489ab9 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-11T04:11:56Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-7c477a63c39542afb73ddfed9a489ab92023-01-01T05:31:59ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042022-12-011812e101054010.1371/journal.pgen.1010540Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection.Martin JankowiakFritz H ObermeyerJacob E LemieuxThe global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance data make rigorous analysis challenging. To meet this challenge, we develop Bayesian Viral Allele Selection (BVAS), a principled and scalable probabilistic method for inferring the genetic determinants of differential viral fitness and the relative growth rates of viral lineages, including newly emergent lineages. After demonstrating the accuracy and efficacy of our method through simulation, we apply BVAS to 6.9 million SARS-CoV-2 genomes. We identify numerous mutations that increase fitness, including previously identified mutations in the SARS-CoV-2 Spike and Nucleocapsid proteins, as well as mutations in non-structural proteins whose contribution to fitness is less well characterized. In addition, we extend our baseline model to identify mutations whose fitness exhibits strong dependence on vaccination status as well as pairwise interaction effects, i.e. epistasis. Strikingly, both these analyses point to the pivotal role played by the N501 residue in the Spike protein. Our method, which couples Bayesian variable selection with a diffusion approximation in allele frequency space, lays a foundation for identifying fitness-associated mutations under the assumption that most alleles are neutral.https://doi.org/10.1371/journal.pgen.1010540 |
| spellingShingle | Martin Jankowiak Fritz H Obermeyer Jacob E Lemieux Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. PLoS Genetics |
| title | Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. |
| title_full | Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. |
| title_fullStr | Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. |
| title_full_unstemmed | Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. |
| title_short | Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. |
| title_sort | inferring selection effects in sars cov 2 with bayesian viral allele selection |
| url | https://doi.org/10.1371/journal.pgen.1010540 |
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