Phylodynamics and molecular evolution of SARS-CoV-2


Trevor Bedford (@trvrb)
Associate Professor, Fred Hutchinson Cancer Research Center
2 Dec 2021

Slides at:

1. Real-time tracking of SARS-CoV-2 evolution

2. Emergence of variants of concern

3. Current circulation patterns

4. Assessing adaptive evolution

5. Variant transmission dynamics

6. Omicron variant

Real-time tracking of SARS-CoV-2 evolution

Over 5.5M 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, Jover Lee, Cassia Wagner, Denisse Sequeira, Cornelius Roemer, Victor Lin, Jennifer Chang

SARS-CoV-2 lineages establish globally in February and March

Limited early mutations like spike D614G spread globally during initial wave

Mutations in summer and fall 2020 were confined to regional dominance

Emergence of variants of concern

Repeated emergence of 484K and 501Y across the world

Emergence of Alpha (B.1.1.7) in the UK

Emergence of Beta (B.1.351) in the South Africa

Emergence of Gamma (P.1) in the Brazil

Working hypothesis of within-host evolution occurring during infection, driven by natural selection against host immunity

Rapid within-host evolution during persistent infection

484K and 501Y observed during this evolution

Current circulation patterns

Spread of VOC / VOI lineages across the world

Delta outcompeting other variants and seemed poised 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

S1 is quickly evolving and highly correlated with clade growth

dN/dS to root further highlights adaptive evolution

Rapid pace of adaptive evolution relative to H3N2 influenza

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

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

Omicron variant

Lineage B.1.1.539 / clade 21K / Omicron variant emerging from basal diversity

Long branch connecting closest sequenced viruses

Omicron viruses with huge excess of mutations in S1

But a slight paucity of divergence in the rest of the genome

Closing thoughts


SARS-CoV-2 genomic epi: Data producers from all over the world, GISAID and the Nextstrain team

Omicron: Tulio de Oliveira and team, Centre for Epidemic Response & Innovation, National Institute For Communicable Diseases in South Africa and the Botswana Harvard HIV Reference Laboratory

Bedford Lab: John Huddleston, James Hadfield, Katie Kistler, Louise Moncla, Maya Lewinsohn, Thomas Sibley, Jover Lee, Cassia Wagner, Miguel Paredes, Nicola Müller, Marlin Figgins, Denisse Sequeira, Victor Lin, Jennifer Chang