Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection

Abstract

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 distinctly 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. 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.

Vaccine breakthrough infection leads to distinct profiles of neutralizing antibody responses by SARS-CoV-2 variant

“…Nevertheless, because estimates are consistent in showing a 30%-40% growth advantage per viral generation of BA.2 over BA.1 (23,24), and BA.1 appears to derive much of its fitness advantage from antibody escape (23), these modest differences in neutralization titers may be epidemiologically significant. As the emergence of new variants continues -with BA.1 replaced by BA.2, which has been since been replaced by its sublineages BA.2.12.1 and BA.4/5 -it is important to characterize the cross-neutralization properties of these variants in unvaccinated, vaccinated, and hybrid immune populations, particularly as the current phase of the pandemic appears to be driven by antibody escape (21,23,27,28). Among vaccinated persons, "breakthrough" infections are not unexpected; the primary goal of vaccination has been protection against severe disease rather than blocking transmission.…”

The potential of genomics for infectious disease forecasting

“…Similarly, a challenge of existing phylogenetic and phylodynamic approaches, such as those using birth-death and coalescent models, is that they usually assume that genetic diversity is phenotypically neutral and are therefore not well suited to forecast resistance or antigenic evolution. There are modelling approaches that account for different growth or death rates in different lineages, including multi-strain epidemiological models, genome-scale negative frequency-dependent selection models 76,77 , multi-type birth-death models 41 , the binary-state speciation and extinction framework 87 and its extensions, and estimates of selection coefficients or fitness using genomic data 17,88 . These may group sequences into types or variants and proceed with an assumption of phenotypic neutrality within these types or focus on identifying mutations that confer an advantage.…”