SARS-CoV-2 variants and
evolutionary dynamics
Trevor Bedford (@trvrb)
Associate Professor
Fred Hutchinson Cancer Research Center
18 Oct 2021
1. Emergence and spread of variants of concern
2. Assessing adaptive evolution
3. Variant transmission dynamics
Emergence and spread of variants of concern
Over 3.4M SARS-CoV-2 genomes shared to GISAID and evolution tracked in real-time at nextstrain.org
Richard Neher,
Emma Hodcroft,
James Hadfield,
Thomas Sibley,
John Huddleston,
Ivan Aksamentov,
Moira Zuber,
Eli Harkins,
Jover Lee,
Cassia Wagner,
Louise Moncla,
Misja Ilcisin,
Kairsten Fay,
Allison Black,
Miguel Paredes,
Sidney Bell,
Denisse Sequeira
SARS-CoV-2 lineages establish globally in Feb and Mar 2020
Limited early mutations like spike D614G spread globally during initial wave
After initial wave, with mitigation
efforts and decreased travel,
regional clades emerge
Mutations in summer and fall 2020 were confined to regional dominance
Repeated emergence of 484K and 501Y across the world
Spread of VOC / VOI lineages across the world
Delta is outcompeting other variants and is on track to sweep
Variants show excess mutations across the genome
But show most substantial excess in the S1 domain of spike
Assessing adaptive evolution
Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2
Katie Kistler,
John Huddleston
Phylogeny of 10k genomes equitably sampled in space and time
Measure clade growth as a proxy for viral fitness
Clades with more S1 nonsynonymous mutations grow faster
This correlation is absent in control regions
This correlation is stronger in S1 than other genes
S1 is quickly evolving and highly correlated with clade growth
dN/dS through time further highlights adaptive evolution
Rapid pace of adaptive evolution relative to H3N2 influenza
S1 mutations cluster phylogenetically, suggesting bursts of evolution
Many S1 mutations show strong convergent evolution
Mutations in S1 arising via within-host pressures result increase viral fitness and are enriched in the viral population by natural selection
Although selection has not been primarily for antigenic drift, observed level of adaptability suggests its potential
Variant transmission dynamics
Multiple approaches to modeling fitness differences between circulating variants
Multinomial logistic regression models work well on frequencies
However, frequency of a variant may rise while cases fall
SARS-CoV-2 variant dynamics across US states show consistent differences in transmission rates
Marlin Figgins
Estimation of variant-specific Rt through time using state-level data
State-level case counts are partitioned based on frequencies of sequenced cases
Partitioned case counts used to estimate variant-specific Rt through time
Differences in intrinsic Rt across variants, but all trending downwards
Consistent differences in variant-specific transmission rate across states
Consistent reductions in variant-specific Rt from vaccination
Future work would ideally tie together granular empirical estimates of viral fitness from frequency data together with mutational and phenotypic predictors to learn what is driving variant success
Generally expect a transition from evolution being driven by selection for increased transmission to sustained selection for antigenic drift
Acknowledgements
SARS-CoV-2 genomic epi: Data producers from all over the world, GISAID and the Nextstrain team
Bedford Lab:
John Huddleston,
James Hadfield,
Katie Kistler,
Louise Moncla,
Maya Lewinsohn,
Thomas Sibley,
Jover Lee,
Cassia Wagner,
Miguel Paredes,
Nicola Müller,
Marlin Figgins,
Eli Harkins,
Denisse Sequeira
