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

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

Figgins and Bedford. Unpublished.

Differences in intrinsic Rt across variants, but all trending downwards

Figgins and Bedford. Unpublished.

Consistent differences in variant-specific transmission rate across states

Figgins and Bedford. Unpublished.

Consistent reductions in variant-specific Rt from vaccination

Figgins and Bedford. Unpublished.

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


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