Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus

Abstract

A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to the introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus, and support continuing surveillance of Spike mutations to aid in the development of immunological interventions.

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Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of
the COVID-19 virus
B. Korber, W.M. Fischer, S. Gnanakaran, H. Yoon, J. Theiler, W. Abfalterer, N.
Hengartner, E.E. Giorgi, T. Bhattacharya, B. Foley, K.M. Hastie, M.D. Parker, D.G.
Partridge, C.M. Evans, T.M. Freeman, T.I. de Silva, C. McDanal, L.G. Perez, H. Tang,
A. Moon-Walker, S.P. Whelan, C.C. LaBranche, E.O. Saphire, D.C. Montefiori, on
behalf of the Sheffield COVID-19 Genomics Group
PII: S0092-8674(20)30820-5
DOI: https://doi.org/10.1016/j.cell.2020.06.043
Reference: CELL 11502
To appear in: Cell
Received Date: 29 April 2020
Revised Date: 10 June 2020
Accepted Date: 26 June 2020
Please cite this article as: Korber, B, Fischer, W., Gnanakaran, S, Yoon, H, Theiler, J, Abfalterer, W,
Hengartner, N, Giorgi, E., Bhattacharya, T, Foley, B, Hastie, K., Parker, M., Partridge, D., Evans, C.,
Freeman, T., de Silva, T., McDanal, C, Perez, L., Tang, H, Moon-Walker, A, Whelan, S., LaBranche, C.,
Saphire, E., Montefiori, D., on behalf of the Sheffield COVID-19 Genomics Group, Tracking changes in
SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus, Cell (2020), doi:
https://doi.org/10.1016/j.cell.2020.06.043.
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Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases
infectivity of the COVID-19 virus
Korber B
1,2
, Fischer WM
1
, Gnanakaran S
1
,
Yoon H
1
, Theiler J
1
, Abfalterer W
1
,
Hengartner N
1
, Giorgi EE
1
, Bhattacharya T
1
, Foley B
1
, Hastie KM
3
, Parker MD
4
,
Partridge DG
5
, Evans CM
5
, Freeman TM
4
, de Silva TI
5,6
, on behalf of the Sheffield
COVID-19 Genomics Group
#
, McDanal C
7
, Perez LG
7
, Tang H
7
, Moon-Walker A
3,8,9
,
Whelan SP
9
, LaBranche CC
7
, Saphire EO
3
, and Montefiori DC
7
1
Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
2
New Mexico Consortium, Los Alamos, MN 87545, USA.
3
La Jolla Institute for Immunology, La Jolla, CA 92037 USA
4
Sheffield Biomedical Research Centre & Sheffield Bioinformatics Core, University of Sheffield, Sheffield, S10
2HQ UK
5
Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, S10 2JF
UK
6
Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield,
Sheffield, S10 2RX
UK
7
Duke Human Vaccine Institute & Department of Surgery, Durham, NC 27710 USA
8
Program in Virology, Harvard University, Boston, MA 02115 USA
9
Department of Molecular Microbiology, Washington University in Saint Louis, St. Louis MO 63130 USA
#
Members of Sheffield COVID-19 Genomics Group: Adrienne Angyal, Rebecca L. Brown, Laura Carrilero, Luke
R. Green, Danielle C. Groves, Katie J Johnson, Alexander J Keeley, Benjamin B Lindsey, Paul J Parsons,
Mohammad Raza, Sarah Rowland-Jones, Nikki Smith, Rachel M. Tucker, Dennis Wang, Matthew D. Wyles
Corresponding Author and Lead Contact: Bette Korber, btk@lanl.gov
Summary. A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become
the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a
recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal.
The shift occurred even in local epidemics where the original D614 form was well established prior to
the introduction of the G614 variant. The consistency of this pattern was highly statistically significant,
suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant
grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-
PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with
increased disease severity. These findings illuminate changes important for a mechanistic
understanding of the virus, and support continuing surveillance of Spike mutations to aid in the
development of immunological interventions.
KEYWORDS: COVID-19; SARS-CoV-2; diversity; evolution; Spike, antibody; infectivity;
neutralization

2
Introduction:
The past two decades have seen three major pathogenic zoonotic disease outbreaks caused by
betacoronaviruses
(Cui et al., 2019; de Wit et al., 2016; Liu et al., 2020; Wu et al., 2020). Severe
Acute Respiratory Syndrome Coronavirus (SARS-CoV) emerged in 2002, infecting ~8,000 people
with a 10% mortality. Middle East Respiratory Syndrome Coronavirus, MERS-CoV, emerged in 2012,
with ~2,300 cases and a 35% mortality (Graham and Baric, 2010). The third, SARS-CoV-2, causes
the severe respiratory disease COVID-19 (Gorbalenya et al., 2020). First reported in China in
December 2019 (Zhou et al., 2020), it rapidly become a pandemic of devastating impact. The June
21, 2020 World Health Organization (WHO) Situation Report records over 8.7 million COVID-19
cases and 460,000 deaths, numbers that increase daily. Human beings have no direct immunological
experience with SARS-CoV-2, leaving us vulnerable to infection and disease. SARS-CoV-2 is highly
transmissible: R
0
,
estimates
vary between 2.2 and 3.9
(Lv et al., 2020). Estimates of mortality vary
regionally between 0.8 and 14.5% (Mortality Analyses, John Hopkins University of Medicine)
Coronaviruses have genetic proofreading mechanisms (Sevajol et al., 2014; Smith et al., 2013),
and
SARS-CoV-2 sequence diversity is very low (Fauver et al., 2020)
.
Still, natural selection can act
upon rare but favorable mutations. By analogy,
antigenic drift
results in
the gradual accumulation of
mutations by the influenza virus during a flu season, and
the complex interplay between
immunological resistance mutations and the fitness landscape enables
antibody resistance to
develop across populations (Wu et al., 2020c), driving the need to develop new influenza
vaccines every few seasons. Longer flu seasons allow increased opportunity for selection
pressure
(Boni et al., 2006). Although the
SARS-CoV-2 shows evidence of some seasonal waning
(Sehra et al., 2020), the persistence of the pandemic may enable accumulation of
immunologically relevant mutations in the population even as vaccines are developed.
Antigenic
drift is seen among common cold coronaviruses OC43 (Ren et al., 2015; Vijgen et al., 2005) and
229E (Chibo and Birch, 2006), and in SARS-CoV-1
(Guan et al., 2003; Song et al., 2005).
Notably, a single SARS-CoV-1 amino acid change, Spike D480A/G in the
receptor binding
domain (RBD)
, arose in infected humans and civets and became the dominant variant among
2003/2004 viruses. D480A/G escapes neutralizing antibody 80R, and immune pressure from
80R in vitro could recapitulate emergence of the D480 mutation
(Sui et al., 2008). Though there is
no evidence yet for antigenic drift for
SARS-CoV-2, with extended human-to-human transmission,
SARS-CoV-2 could also acquire mutations with fitness advantages and immunological
resistance. Attending to this risk now by identifying evolutionary transitions that may be relevant

3
to the fitness or antigenic profile of the virus is important to ensure the effectiveness of the
vaccines and immunotherapeutic interventions as they advance to the clinic.
In response to the urgent need to develop effective vaccines and antibody-based therapeutics
against SARS-CoV-2, over 90 vaccine and 50 antibody approaches are currently being explored
(Cohen, 2020; Yu et al., 2020). Most target the trimeric Spike protein, which mediates host cell
binding and entry and is the major target of neutralizing antibodies (Chen et al., 2020; Yuan et al.,
2020). Spike monomers are comprised of an N-terminal S
1
subunit that mediates receptor binding
and a membrane-proximal S
2
subunit that mediates membrane fusion
(Hoffmann et al., 2020a; Walls
et al., 2020; Wrapp et al., 2020). SARS-CoV-2 and SARS-CoV-1 share ~79% sequence identity
(Lu
et al., 2020) and both use angiotensin converting enzyme-2 (ACE2) as their cellular receptor.
Antibody responses to the SARS-CoV-1 Spike are complex. In some patients with rapid and high
neutralizing antibody responses, an early decline of these responses was associated with increased
severity of disease and a higher risk of death (Ho et al., 2005; Liu et al., 2006; Temperton et al.,
2005; Zhang et al., 2006). Some antibodies against SARS-CoV-1 Spike mediate antibody-dependent
enhancement (ADE) of infection in vitro and exacerbate disease in animal models (Jaume et al.,
2011; Wan et al., 2020; Wang et al., 2014; Yip et al., 2016).
Most current SARS-CoV-2 immunogens and testing reagents are based on the Spike protein
sequence of the Wuhan reference sequence (Wang et al., 2020), and first-generation antibody
therapeutics were discovered based on the early pandemic infections and evaluated using the
Wuhan reference sequence proteins. Alterations from the reference sequence as the virus
propagates in human-to-human transmission could potentially alter the viral phenotype and/or the
efficacy of immune-based interventions. Therefore, we have designed bioinformatic tools to create an
“early warning” strategy to evaluate Spike evolution during the pandemic, to enable the testing of
mutations for phenotypic implications and the generation of appropriate antibody breadth evaluation
panels as vaccines and antibody-based therapeutics progress. Phylogenetic analysis of the global
sampling of SARS-CoV-2 is being very capably addressed at the Global Initiative for Sharing All
Influenza Data (GISAID) database (www.gisaid.org; Elbe and Buckland-Merrett, 2017; Shu and
McCauley, 2017) and Nextstrain (nextstrain.org; (Hadfield et al., 2018). In a setting of low genetic
diversity like that of SARS-CoV-2, however, with very few de novo mutational events, phylogenetic
methods that use homoplasy to identify positive selection (Crispell et al., 2019)
have limited statistical
power. Additionally, recombination can add a confounding factor to phylogenetic reconstructions, and
recombination is known to play a role in natural coronavirus evolution (Graham and Baric, 2010; Lau

4
et al., 2011; Li et al., 2020; Oong et al., 2017; Rehman et al., 2020), and recombinant sequences
(although potential sequencing artefacts) have been found among SARS-CoV-2 sequences (De Maio
et al., 2020). Given these issues, we developed an alternative indicator of potential positive selection,
by identifying variants that are recurrently becoming more prevalent in different geographic locations.
If increases in relative frequency of a particular variant are repeatedly observed in distinct geographic
regions, that variant becomes a candidate for conferring a selective advantage.
Single amino acid changes are worth monitoring as they can be phenotypically relevant. Among
coronaviruses, point mutations have been demonstrated to confer resistance to neutralizing
antibodies in MERS-CoV (Tang et al., 2014) and SARS-CoV-1 (Sui et al., 2008; ter Meulen et al.,
2006). In the HIV Envelope, single amino acid changes are known to alter host species susceptibility
(Li et al., 2016), increase expression levels (Asmal et al., 2011), change viral phenotype from Tier 2
to Tier 1, causing an overall change in neutralization sensitivity (Gao et al., 2014; LaBranche et al.,
2019), and confer complete or near complete resistance to classes of neutralizing antibodies (Bricault
et al., 2019; Sadjadpour et al., 2013; Zhou et al., 2019).
We have developed a bioinformatic pipeline to identify Spike amino acid variants that are increasing
in frequency across many geographic regions by monitoring GISAID data. By early April 2020, it was
clear the Spike D614G mutation was exhibiting this behavior, and G614 has since become the
dominant form in the pandemic. We present experimental evidence that the G614 variant is
associated with greater infectivity, as well as clinical evidence that it is associated with higher viral
loads. We continue to monitor other mutations in Spike for frequency shifts at regional and global
levels and to provide regular updates at a public web site (cov.lanl.gov).
Results
Website Overview
Our analysis pipeline to track SARS-CoV-2 mutations in the COVID-19 pandemic is based on regular
updates from the GISAID SARS-CoV-2 sequence database (GISAID acknowledgments are in Table
S1). GISAID sequences are generally linked to the location and date of sampling. Our website
provides visualizations and summary data that allow regional tracking of SARS-CoV-2 mutations over
time. Hundreds of new SARS-CoV-2 sequences are added to GISAID each day, so we have
automated steps to create daily working alignments (Kurtz et al., 2004) (Fig. S1). The analysis

5
presented here is based on a May 29, 2020 download of the GISAID data, when our Spike alignment
included 28,576 sequences; updated versions of key figures can recreated at our website
(cov.lanl.gov). The overall evolutionary rate for SARS-CoV-2 is very low, so we set a low threshold for
a Spike mutation to be deemed “of interest”, and we track all sites in Spike at which 0.3% of the
sequences differ from the Wuhan reference sequence, monitoring them for increasing frequency over
time in geographic regions, as well as for recurrence in different geographic regions. Here we present
results for the first amino acid variant to stand out by these metrics, D614G.
The D614G variant
Increasing frequency and global distribution. The Spike D614G amino acid change is caused by
an A-to-G nucleotide mutation at position 23,403 in the Wuhan reference strain; it was the only site
identified in our first Spike variation analysis in early March that met our threshold criterion. At that
time, the G614 form was rare globally, but gaining prominence in Europe, and GISAID was also
tracking the clade carrying the D614G substitution, designating it the “G clade”. The D614G change is
almost always accompanied by three other mutations: a C-to-T mutation in the 5’ UTR (position 241
relative to the Wuhan reference sequence), a silent C-to-T mutation at position 3,037; and a C-to-T
mutation at position 14,408 that results in an amino acid change in RNA-dependent RNA polymerase
(RdRp P323L). The haplotype comprising these 4 genetically linked mutations is now the globally
dominant form. Prior to March 1, it was found in 10% of 997 global sequences; between March 1-
March 31, it represented 67% of 14,951 sequences; and between April 1- May 18 (the last data point
available in our May 29
th
sample) it represented 78% of 12,194 sequences. The transition from D614
to G614 was occurred asynchronously in different regions throughout the world, beginning in Europe,
followed by North America and Oceania, then Asia (Figs. 1-3, S2-S3).
We developed two statistical approaches to assess the consistency and significance of the D614-to-
G614 transition. In general, to observe a significant change in the frequency of variants in a
geographic region, three requirements must be met. First, both variants must at some point be co-
circulating in the geographic area. Second, there must be sampling over an adequate duration to
observe a change in frequency. Third, enough samples must be available for adequate statistical
power to detect a difference. Both of our approaches enable us to systematically extract all GISIAD
local and regional data that meet these three requirements.

6
Our first approach requires that there be an “onset”, defined as the first day where the cumulative
number of sequences reached 15 and both forms were represented at least 3 times; we further
require that there be at least 15 sequences available at least two weeks after the onset. Each
geographic region that meets these criteria is extracted separately based on the hierarchical
geographic/political levels designated in GISAID (Fig. 1B). A two-sided Fisher's exact test compares
the counts in the pre-onset period to the counts after the two-week delay period and provides a p-
value against the null hypothesis that the fraction of D614 vs G614 sequences did not change. All
regions that met the above criteria and that showed significant change in either direction (p<0.05) are
included. Almost all shifted towards increasing G614 frequencies: 5/5 continents, 16/17 countries,
(two-sided binomial p-value of 0.00027); 16/16 regions (p = 0.00003), and 11/12 counties and cities
(p= 0.0063).
In Fig. 2 (Europe), Fig. S2 (North America), and Fig. S3 (Australia and Asia), we break down the
relationships shown in Fig. 1B in detail. The G614 variant increased in frequency even in regions
where D614 was the clearly dominant form of a well-established local epidemic at the time G614
entered the population. Examples of this scenario include Wales, Nottingham, and Spain (Fig. 2);
Snohomish county and King county (Fig. S2); and New South Wales, China, Japan, Hong Kong, and
Thailand (Fig. S3). While introduction of a new variant might sometimes result in emergence of the
new form due to stochastic effects or serial re-introductions, or apparent emergence due to sampling
biases, the consistency of the shift to G614 across regions is striking. The increase in G614 often
continued after national stay-home-orders were implemented, and in some cases beyond the 2-week
maximum incubation period.
We found two exceptions to the pattern of increasing G614 frequency in Figure 1B; details regarding
these cases are shown in Figure S4. The first is Iceland. Changes in sampling strategy during a
regional molecular epidemiology survey conducted through the month of March might explain this
exception
(Gudbjartsson et al., 2020). In early March, only high-risk people were sampled, the
majority being travelers from countries in Europe where G614 dominated. In mid-March, screening
began to include the local population; this coincided with the appearance of the D614 variant in the
sequence data set. The second exception is Santa Clara county, one of the most heavily sampled
regions in California (Deng et al., 2020). The D614 variant dominates sequences from the Santa
Clara Department of Public Health (DPH) to date; the G614 variant was apparently not established in
that community. In contrast, a smaller set of Santa Clara county sequences, sampled mid-March to
early April, were specifically noted to be from Stanford: the Stanford samples had a mixture of both

7
forms co-circulating (Fig. S4), suggesting that the two communities within Santa Clara County are
effectively distinct. A June 19
th
GISAID update for several California counties is provided in Figure
S4C, and the G614 form is present in the most recent Santa Clara DPH samples.
Our second statistical approach to evaluating the significance of the D614-to-G614 transition (Fig. 3)
uses the time-series data in GISAID more fully. Here we extracted all regional data from GISAID that
had a minimum of 5 sequences representing each of the D614 and the G614 variants, and at least 14
days of sampling. We then modeled the daily fraction of G614 as a function of time using isotonic
regression, testing the null hypothesis that this fraction does not change over time (i.e. it remains
roughly flat over time, with equally likely random fluctuations of increase or decrease). We then
separately tested the null against two alternative hypotheses: that the fraction of G614 either
increases, or that it decreases. Figure 3A shows separate p-values for all subcountries/states and
counties/cities that met the minimal criteria. 30 of 31 subcountries/states with a significant change in
frequency were increasing in G614; a binomial test indicates that G614 increases are highly
significantly enriched (p-value = 2.98e-09). This was also found in 17 of 19 counties/cites (p-value =
0.0007). Figure 3B shows examples for 3 cities, plotting the daily fraction of G614 as a function of
time. Country summaries (similar to Fig. 3A), and plots for all regions (similar to Fig. 3B) are included
in Data S1.
Origins of the D614G 4-base haplotype. The earliest examples of sequences carrying parts of the
4-mutation haplotype that characterizes the D614G GISAID G clade were found in China and
Germany in late January, and they carried 3 of the 4 mutations that define the clade, lacking only the
RdRp P323L substitution (Fig. S5D). This may be an ancestral form of the G clade. One early Wuhan
sequence and one early Thai sequence had the D614G change, but not the other 3 mutations (Fig.
S5D); these may have arisen independently. The earliest sequence we detected that carried all 4
mutations was sampled in Italy on Feb. 20 (Fig. S5D). Within days, this haplotype was sampled in
many countries in Europe.
Structural implications of the Spike D614G change. D614 is located on the surface of the Spike
protein protomer, where it can form contacts with the neighboring protomer (Fig. 4A). Cryo-EM
structures
(Walls et al., 2020; Wrapp et al., 2020)
indicate that the sidechain of D614 and T859 of the
neighboring protomer (Fig. 4B) form a between-protomer hydrogen bond, bringing together a residue
from the S
1
unit of one protomer and a residue of the S
2
unit of the other protomer (Fig. 4C). The
change to G614 would eliminate this sidechain hydrogen bond, possibly increasing mainchain

8
flexibility and altering between-protomer interactions. In addition, this substitution could modulate
glycosylation at the nearby N616 site, influence the dynamics of the spatially proximal fusion peptide
(Fig. 4D) of the neighboring protomer, or have other effects.
G614 is associated with potentially higher viral loads in COVID-19 patients but not with
disease severity. SARS-CoV-2 sequences from 999 individuals presenting with COVID-19 disease
at the Sheffield Teaching Hospitals NHS Foundation Trust were available, and linked to clinical data.
The Sheffield data include age, sex, date of sampling, hospitalization status (defined as outpatient,
OP; inpatient, IP, requiring hospitalization; or admittance into the intensive care unit, ICU), and the
cycle threshold (Ct) for a positive signal in E-gene based RT-PCR. The Ct is used here as a
surrogate for relative viral loads; lower Ct values indicate higher viral loads (Corman et al., 2020),
though not all viral nucleic acids represent infectious viral particles. RT-PCR methods changed during
the course of the study due to limited availability of testing kits. The first method involved nucleic acid
extraction; the second method, heat treatment (Fomsgaard and Rosenstierne, 2020). A generalized
linear model (GLM) used to predict PCR Ct based on the RT-PCR method, sex, age, and D614G
status showed only the RT-PCR method (p <2e-16) and D614G status (p=0.037) to be statistically
significant (Fig. 5A). Lower Ct values were observed in G614 infections. While our paper was in
revision, G614-variant association with low Ct values in vivo (Fig. 5) was independently reported by
two other groups (Lorenzo-Redondo et al., 2020; Wagner et al., 2020), in preprints that have not yet
been peer reviewed.
We found no significant association between D614G status and disease severity as measured by
hospitalization outcomes. A comparison of D614G status and hospitalization (combining IP+ICU) was
not significant (p = 0.66, Fisher’s exact test), although comparing ICU admission with (IP+OP) did
have borderline significance (p= 0.047) (Fig. 5B). Regression analysis reinforced the result that G614
status was not associated with greater levels of hospitalization, but that higher age (Dowd et al.,
2020; Promislow, 2020), male sex (Conti and Younes, 2020; Promislow, 2020) and higher Ct values
(lower viral loads) were each highly predictive of hospitalization. Further analysis showed that viral
load was not masking a potential D614G status effect on hospitalization (see STAR Methods).
Univariate analysis also found highly significant associations between age and male sex and
hospitalization (see STAR Methods).
G614 is associated with higher infectious titers of spike-pseudotyped virus. We quantified the
infectious titers of pseudotyped single-cycle vesicular stomatitis virus (VSV) and lentiviral particles

9
displaying either D614 or G614 SARS-CoV2 Spike protein. For both the VSV and lentiviral
pseudotypes, G614-bearing viruses had significantly higher infectious titers (2.6 – 9.3 fold increase)
than their D614 counterparts; this was confirmed in multiple cell types (Figure 6A-C). Similar results
recently reported in a preprint that has not yet been peer reviewed also suggest that G614 increases
both spike stability and membrane incorporation
(Zhang et al., 2020).
TMPRSS2, a type-II transmembrane serine protease, cleaves the viral spike after receptor binding to
enhance entry of MERS-CoV, SARS-CoV and SARS-CoV-2 (Hoffmann et al., 2020b; Kleine-Weber
et al., 2018; Matsuyama et al., 2020; Millet and Whittaker, 2014; Park et al., 2016; Shulla et al., 2011;
Zang et al., 2020). Spike 614 is in a pocket adjacent to the fusion peptide near the expected
TMPRSS2 cleavage site, suggesting there could be differences in the propensity and/or requirement
for TMPRSS2 of the G614 variant. To test this hypothesis, we infected 293T cells stably expressing
ACE2 receptor in the presence or absence of TMPRSS2, and quantified the titer of infectious virus.
We found similar fold-changes in the titers between D614 and G614 regardless of TMPRSS2
expression (Fig. 6A). Hence, entry of G614-bearing viruses in 293T-ACE2 cells, as compared to
D614-bearing viruses, is not enhanced by TMPRSS2. Further studies are required to determine if the
G614 variant shows increased titers in lung cells, which may recapitulate native protease expression
levels more faithfully, and to determine if this variant increases the fitness of authentic SARS-CoV-2.
We also tested if the D614G variations would be similarly neutralized by polyclonal antibody. The
convalescent sera of six San Diego residents, likely infected in early to mid-March when both D614
and G614 were circulating, each demonstrate equivalent or better neutralization of G614-bearing
pseudovirus compared to D614-bearing pseudovirus (Fig 6D-E). Although we do not know with which
virus each of these individuals were infected, these initial data suggest that despite increased fitness
in cell culture, G614-bearing virions are not intrinsically more resistant to neutralization by
convalescent sera.
Additional sites of interest in the Spike gene with rare mutations
Spike has very few mutations overall. A small set has reached >0.3% of the global population
sample, the threshold for automatic tracking at the cov.lanl.gov website (Fig. 7A and B, details
provided in Table S2). Regions in the alignment where entropy is relatively high compared to the rest
of Spike (i.e., local clusters of rare mutations) are also tracked (Table S2). Genetic mutations of
interest are mapped as amino acid changes onto a Spike structure (Figure 4). The mutation resulting
in the signal peptide L5F change recurs many times in the tree and is stably maintained in about
0.6% of the global GISAID data. There are several clusters of mutations in region of the spike gene

10
encoding the N-terminal domain (NTD) and RBD which are potential targets for neutralizing
antibodies (Chen et al., 2017; Zhou et al., 2019a; (Sui et al., 2008; Tang et al., 2014; ter Meulen et
al., 2006). The RBD cluster (positives 475-483) spans two positions, at 475 and 476, that are located
within 4Å of bound ACE2 (Fig. 4D) (Yan et al., 2020). The fusion peptide contains a cluster of amino
acid changes between 826-839; this cluster is highlighted in Fig. 7 to illustrate our web-based tools
for tracking variation (Fig. 7A-C). The fusion core of HR1 (Xia et al., 2020), next to the helix break in
pre-fusion Spike, also contains a cluster of amino acid changes between 93E-940 (Fig. 4E). The motif
SXSS (937-940) may enhance the association of helices
(Dawson et al., 2002; Salamango and
Johnson, 2015). The cytoplasmic tail of Spike also contains a site of interest, P1263L.
Discussion
Our data show that over the course of one month, the variant carrying the D614G Spike mutation
became the globally dominant form of SARS-CoV-2. Phylogenetic tracking of SARS-CoV-2 variants
at Nextstrain reveals complex webs of evolutionary and geographical relationships (nextstrain.org;
(Hadfield et al., 2018)); travelers dispersed G614 variants globally, and likely would have introduced
and reintroduced G614 variants into different locations. Still D614 prevalent epidemics were very well
established in many locations when G614 first began to appear (see Fig. S2 for examples). The
mutation that causes the D614G amino change is transmitted as part of a conserved haplotype
defined by 4 mutations that almost always track together (Fig. S5 and S6). The pattern of increasing
G614 frequency within many different populations where both D614 and G614 were co-circulating is
highly significant, suggesting that G614 may be under positive selection (Fig. 1b, 3). We also found
G614 to be associated with higher levels of viral nucleic acid in the upper respiratory tract in human
patients (Fig. 5) (suggestive of higher viral loads), and with higher infectivity in multiple pseudotyping
assays (Fig. 6).
Given that most G614 variants belong to the G clade lineage, phylogenetic methods that depend
upon recurrence of mutational events for their signal are poorly powered to resolve whether D614G is
under positive selection. The GISAID data, however, provided the opportunity to look into the
relationships among the SARS-CoV-2 variants in the context of time and geography, enabling us to
track the increase in frequency of G614 as an early indicator of possible positive selection. This
approach is potentially subject to founder effects and sampling biases, and so we generally view this
strategy as simply an early indicator of an amino acid change that should be monitored further and
tested. The G614 variant stood out, however, in our early detection framework for several reasons.
First was the consistency of increase across geographic regions, which was highly significantly non-

11
random (Figs. 1b and Fig. 3). Second, if the two forms were equally likely to propagate, one would
expect the D614 form to persist in many locations where the G614 form was introduced into the
ongoing well-established D614 epidemics. Instead, we found that even in such cases, G614
increased (Figs. 1-3, S2 and S3.). Third, the increase in G614 frequency often continued well after
national stay-at-home orders were in place, when serial reseeding from travelers was likely to be
significantly reduced (Figs. 2, S2 and S3.).
Our global tracking data show that the G614 variant in Spike has spread faster than D614. We
interpret this to mean that the virus is likely to be more infectious, a hypothesis consistent with the
higher infectivity observed with G614 Spike-pseudotyped viruses we observed in vitro (Fig. 6), and
the G614 variant association with higher patient Ct values, indicative of potentially higher in vivo viral
loads (Fig. 5). Interestingly, we did not find evidence of G614 impact on disease severity; i.e., it was
not significantly associated with hospitalization status. However, an association between the G614
variant and higher fatality rates has been reported in a comparison of mortality rates across countries,
although this kind of analysis can be complicated by different availability of testing and care in
different nations (Becerra-Flores and Cardozo, 2020).
While higher infectiousness of the G614 variant may fully account for its rapid spread and
persistence, other factors should also be considered. These include epidemiological factors, as viral
spread also depends on who it infects, and epidemiological influences can also cause changes in
genotype frequency to mimic evolutionary pressures. In all likelihood, a combination of evolutionary
selection for G614 and the founder’s effects of being introduced into highly mobile and connected
populations may have together contributed in part to its rise. The G-clade mutations in the 5’ UTR, or
in the RdRP protein might also have impact. In addition, there could be immunological consequences
resulting from the G614 change in Spike. The G614 variant is sensitive to neutralization by polyclonal
convalescent sera (Fig. 5), which is encouraging in terms of immune interventions, but it will be
important to determine whether the D614 and G614 forms of SARS-CoV-2 are differentially sensitive
to neutralization by vaccine-elicited antibodies or by antibodies produced in response to infection with
either form of the virus. Also, if the G614 variant is indeed more infectious than the D614 form (Fig.
6), it may require higher antibody levels for protection by vaccines or antibody therapeutics than the
D614 form. Antibodies against an immunodominant linear epitope spanning Spike 614 in SARS-CoV-
1 were associated with ADE activity (Wang et al., 2016), and so it is possible that this mutation may
impact ADE.

12
Tracking mutations in the Spike gene has been our primary focus to date because of its relevance to
vaccine and antibody-based therapy strategies currently under development. Such interventions take
months to years to develop. For the sake of efficiency, contemporary variation should be factored in
during development to ensure that the interventions will be effective against circulating variants when
they are eventually deployed. To this end, we built a data-analysis pipeline to enable the exploration
of potentially interesting mutations on SARS-CoV-2 sequences. The analysis is updated daily as the
data become available through GISAID, enabling experimentalists can make use of the most current
data available to inform vaccine development, reagents for evaluating antibody response, and
experimental design. The speed with which G614 variant became the dominant form globally
suggests the need for continued vigilance.
Limitations of this study
Shifts in frequency towards the G614 variant in any given geographic region could in principle result
from either founder effects or sampling biases; it was the consistency of this pattern across regions
where both forms of the virus were initially co-circulating that led us to suggest that the G614 form
might be transmitted more readily due to an intrinsic fitness advantage; however, systematic biases
across many regions could impact the levels of significance we observed. The lack of association
between G614 and hospitalization that we report may miss impacts on disease severity that are more
subtle than we can detect. The experimental approach taken here to acquire laboratory evidence of
increased fitness of the D614G mutation is based on two different pseudovirus models of infection in
established cell lines. The extent to which this model faithfully recapitulates wild-type virus infection in
natural target cells of the respiratory system is still being determined, and our laboratory experiments
do not directly address the biology and mechanics of natural transmission. Infectiousness and
transmissibility are not always synonymous, and more studies are needed to determine if the D614G
mutation actually led to an increase number of infections, not just higher viral loads during infection.
We encourage others to study this phenomenon in greater detail with wild-type virus in natural
infection and varied target cells (Hou et al., 2020), and in relevant animal models. Finally, the
neutralization assays performed were based on sera from SARS-CoV-2 infected individuals with an
unknown D614G status. Thus, while they show that the G614 variants are neutralization sensitive,
more work is needed to resolve whether the potency of neutralization is affected when the variant that
initiated the immune response differs from the test variant, or when monoclonal antibodies are used.

13
Acknowledgements
We thank Andrew McMichael, Sarah Rowland-Jones, and Xiao-Ning Xu for bringing together the
clinical and theory teams. We thank Anthony West for pointing out the 5’ UTR G-clade mutation,
George Ellison for suggestions on clinical data analyses; Barbara Imperiali for insights regarding the
structural implications of the D614G change; and Rachael Mansbach, Srirupa Chakraborty and Kien
Nguyen for sharing preliminary MD data. We thank Alessandro Sette and Shane Crotty for survivor
sera, and Sharon Schendel for manuscript edits. We acknowledge Barney Graham, Kizzmekia
Corbett, Nicole Doria-Rose, Adrian McDermott and John Mascola at the Vaccine Research Center,
NIH for reagents and assistance with the lentiviral-based SARS-CoV-2 infection assay, and Elize
Domin for technical support. The Sheffield COVID-19 Genomics Group are part of the COG-UK
CONSORTIUM, supported by the Medical Research Council (MRC) part of UK Research &
Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited,
operating as the Wellcome Sanger Institute. TIdS is supported by a Wellcome Trust Intermediate
Clinical Fellowship (110058/Z/15/Z). MP was funded by the NIHR Sheffield Biomedical Research
Centre (BRC). BK, EEG, TM, NH, WMF, HY, WA were supported by the LANL LDRD projects
20200554ECR and 20200706ER, and through the NIH NIAID, DHHS Interagency Agreement
AAI12007-001-00000.
EOS acknowledges support of CoVIC, INV-006133 of the COVID-19 Therapeutics
Accelerator, supported by the Bill and Melinda Gates Foundation, Mastercard, Wellcome and private
philanthropic support, the Overton family, and a FastGrant from Emergent Ventures in aid of COVID-19
research.
We gratefully acknowledge the team at GISAID for creating SARS-CoV-2 global database,
and the many people who provided sequence data (Table S1).
Author Contributions
Conceptualization, B.K and D.C.M.; Methodology, B.K., W.M.F, J.T, N.H., E.O.S, and
D.C.M.; Software, W.M.F, J.T., H.Y, W.A., N.H, E.E.G., T.B., T.M.F, M.D.P, and B.K.; Validation,
E.O.S, D.C.M., J.T., B.K., B.F., and N.H.; Formal Analysis, B.K., J.T., N.H., W.M.F, S.G., M.D.P.,
T.M.F, D.G.P., C.M.E, T.I.d.S., T.B. and E.E.G.; Investigation, E.O.S., D.C.M., K.M.H, C.M.E, D.G.P.
L.G.P., H.T., A.M.-W., S.P.W., C.C.L. and T.I.d.S.;
Writing Original Draft, B.K., W.M.F., S.G., D.C.M.,
and E.O.S;
Writing, Review & Editing, T.I.d.S, C.C.L., E.E.G, N.H., H.Y., and T.B; Visualization, B.K.,
E.O.S, J.T., N.H., W.M.F, E.E.G, and S.G.
Supervision, B.K., D.M., E.O.S, T.I.d.S., and S.P.W
;
Funding Acquisition, B.K., E.E.G., D.C.M., S.G., E.O.S, and T.I.d.S.
Declaration of Interests
The authors declare no competing interests.
Main Figure Titles and Legends

14
Fig. 1. The global transition from original D614 form to the G614 variant. A. Changes in the
global distribution of the relative frequencies of the D614 (orange) and G614 (blue) variants in 2
timeframes. Circle size indicates the relative sampling within each map. Through March 1, 2020 the
G614 variant was rare outside of Europe, but the end of March it had increased in frequency
worldwide. These data are explored regionally in Fig. 2 (Europe), Fig. S2 (N. America), and Fig. S3
(Australia and Asia). B. Paired bar charts compare the fraction of sequences with D614 and with
G614 for two time periods separated by a 2-week gap. The first time period (left bar) includes all
sequences up to the "onset" day (see main text). The second time period (right bar) includes all
sequences acquired at least 2 weeks after the onset date. All regions are shown that met the minimal
threshold criteria for inclusion (see main text) with a significant shift in frequency (two-sided Fisher’s
exact test p <0.05). Four hierarchical geographic levels are split out, based on GISAID naming
conventions. C. Running weekly average counts of sampled sequences exhibiting the D614 (orange)
and G614 (blue) variants in different continents between January 12 to May 12. The measure of
interest is the relative frequency over time. The shape of the overall curve just reflects sample
availability: Sequencing was more limited earlier in the epidemic (hence the left-hand tail), and there
is a time lag between viral sampling and sequence availability in GISAID (hence the right-hand tail).
Weekly running count plots were generated with Python Matplotlib (Hunter, 2007); all elements of this
figure are frequently updated at cov.lanl.gov.
Fig. 2. The transition from D614 to G614 in Europe. A. Maps of relative D614 and G614
frequencies in Europe in 2 timeframes. B. Weekly running counts of G614 illustrating the timing of its
spread in Europe. The Fig. 1 caption explains how to read these figures. Some nations were
essentially G614 epidemics when sampling began, but even in these cases small traces of D614
found early on were soon lost (e.g. France and Italy). The Italian epidemic started with D614 clade,
but Italy had the first sampled case of the full G614 haplotype, and had shifted to all G614 samples
prior to March 1 (see Fig. S5). European nations that began with a mixture of D614 and G614 most
clearly reveal the frequency shifts (e.g. Germany, Spain and the United Kingdom). The UK is richly
sampled, and so is subdivided into smaller regions: England, Wales, and Scotland, then further
divided to display two well-sampled English cities. Even in settings with very well-established D614
epidemics (e.g. Wales and Nottingham, also see Figs. S2 and S3), G614 becomes prevalent soon
after its appearance. The increase in G614 frequency often continues well after stay-at-home orders
are in place (pink line) and past the subsequent two-week incubation period (pink transparent box).
The figures shown here can be recreated with contemporary data from GISAID at the cov.lanl.gov

15
website. UK stay-at-home order dates were based on the date of the national proclamation, others
were documented on the web (Schramm and Melin, 2020).
Fig. 3. Modeling the daily fraction of the G614 variant as a function of time in local regions
using isotonic regression. A. Analysis summaries for all of the level 3 and 4 regional subdivisions
from GISAID data (Fig. 1) that have at least 5 each of D614 and G614 variants and that are sampled
on at least 14 days. We report the number of each variant, the number of days with test results, and
the number of days spanning the first and the last reported tests. P-values are for two one-sided
tests, comparing of the null hypothesis of no consistent changes in relative frequency over time either
to positive pressure (the fraction of the G614 variant increasing with time) or to negative pressure (the
fraction of the G614 variant decreasing with time). Across all regions with sufficient data, binomial p-
values against the null that increases and decreases are equally likely indicate that the consistency of
increasing G614 is highly significant. California has both increasing and decreasing patterns with low
p-values; this can happen when different time windows support opposing patterns. The G614
decreasing time window in California was driven by sampling from Santa Clara county, a rare region
that has retained the D614 form (Fig. S4). In the May 29
th
data set used here, Santa Clara county
was sampled later in May than any other region in the California, so the California G614 frequency
dips at this last available time point. If Santa Clara county is removed from the California sample, the
pattern of increasing levels of G614 is restored (red asterisk). B. Three examples for cities, plotting
the daily fraction of G614 as a function of time, and accompanied by plots of running weekly counts.
The dot size is proportional to the number of sequences sampled that day. The staircase line is the
maximum likelihood estimate under the constraint that the logarithm of the odds ratio is non-
decreasing. Two typical examples are shown highlighted in blue (Sydney and Cambridge), and one
exception is shown, highlighted in orange (Yakima). Yakima had a brief sampling window enriched for
G614 early in the sampling period, but otherwise G614 maintained a low frequency. Summaries and
plots for all regional data at levels 2-4 (included country) are included in Data S1.
Fig. 4. Structural mapping of amino acid changes and clusters of variation in the Spike
protein. A. Sites including Spike 614 and those noted in Figure 7 mapped onto S
1
and S
2
units of the
Spike protein (PDB:6VSB). S
1
and S
2
are defined based on the furin cleavage site (protomer #1: S
1
-
dark blue, S
2
-light blue, protomer #2: S
1
-dark green, S
2
-light green, protomer #3: grey). The RBD of
protomer #1 is in the “up” position for engagement with the ACE2 receptor. Sites of interest are
indicated by red balls, and variable clusters are labeled in red. The missing RBD residues at the
ACE2 interface are shown in panel D. B. Proximity of D614 (red) to an N-linked glycosylation sequon

16
of its own protomer (blue) and to residues T859 and Q853 of the neighboring protomer (green) are
shown. Black dashed lines indicate possible hydrogen bond formation. Dotted lines indicate the
structurally unresolved region of the fusion peptide connecting to Q853. C. Schematic representation
of potential protomer-protomer interactions shown in B in which D614 (red) from S
1
unit of one
protomer (blue) is brought close to T859 from the S
2
unit of the neighboring protomer. D. Sites of
interest (red, residues 475-483) near the RBD (blue)-ACE2 (yellow) binding interface. The interfacial
region is shown as a molecular surface (PDB: 6M17). E. Variable cluster 936-940 (red), in the HR1
region of S
2
. These residues occur in a region that undergoes conformational transition during fusion:
pre-fusion (PDB:6VSB) and post-fusion (PDB: 6LXT) conformations of HR1 are shown, left and right.
Fig. 5. Clinical status and D614G associations based on 999 subjects with COVID-19 and
linked sequence and clinical data were sampled in Sheffield, England. A) G614 was associated
with a lower cycle threshold (Ct) required for detection; lower values are indicative of higher viral
loads. The PCR method was changed part way through April due to shortages of nucleic-acid
extraction kits (Fomsgaard and Rosenstierne, 2020). Ct levels for the two PCR methods (nucleic acid
extraction vs. simple heat inactivation) differ, and so we used a GLM to evaluate statistical impact of
D614G across methods. B) D614G status was not statistically associated with hospitalization status
(outpatient (OP), inpatient (IP), or ICU) as a marker of disease severity, but age was highly
correlated. The number of counts in each category is noted in the upper right-hand corner of each
graph. See the main text and methods for statistical details.
Fig. 6. Viral infectivity and D614G associations A) Recombinant VSV pseudotyped with the G614
Spike grows to higher titer than D614 Spike in Vero, 293T-ACE2 and 293T-ACE2-TMPRSS2 cells as
measured in terms of focus forming units (ffu). Four asterisks (****) indicates a p <0.0001 by a
Student’s t test in pairwise comparisons. Experiments were repeated twice, each time in triplicate.
Using a GLM to assess viral infectivity of the D614 and G614 variants across cell types and to
account for repeat experiments, we found the G614 variant had average 3-fold higher infectious titer
than D614, and that this difference was highly significant (p=9x10
-11
) (see STAR Methods). B,C)
Recombinant lentiviruses pseudotyped with the G614 Spike were more infectious than corresponding
D614 S-peudotyped viruses in (B) 293T/ACE2 (6.5-fold increase) and (C) TZM-bl/ACE2 cells (2.8-
fold increase, p <0.0001). Relative luminescence units (RLUs) of Luc reporter gene expression
(Naldini et al., 1996) were standardized to p24 content of the pseudoviruses (p24 content of
pseudoviruses for 293T/ACE2 cells: D614= 269 ng/ml, G614= 255 ng/ml; p24 content of
pseudoviruses for TZM-bl/ACE2 cells, D614= 680 ng/ml, G614= 605 ng/ml). Background RLU was

17
measured in wells that received cells but no pseudovirus. D,E) Convalescent sera from six individuals
in San Diego (4765-4767 and 4774-4777) can neutralize both D614- (orange) and G614- (blue)
bearing VSV pseudoviruses. Samples 1592 and 1616 (grey) are negative control normal human sera.
% relative infection is plotted vs. log polyclonal antibody concentration.
Fig. 7. Tracking variation in Spike. A. Spike sites of interest (with a minimum frequency of 0.3%
variant amino acids) are mapped onto a parsimony tree (for D614G, see Fig. S6). L5F recurs
throughout the tree, and often clustered in small local clades. A829T is found in a single lineage.
Other sites of interest cluster in a main lineage, but are occasionally found in other parts of the tree in
distant geographic regions, thus likely to be recurring at a low level. Build parsimony trees. A brief
parsimony search (parsimony ratchet, with 5 replicates) is performed with 'oblong' (Goloboff, 2014)
This is intended as an efficient clustering procedure rather than an explicit attempt to achieve an
accurate phylogenetic reconstruction, but it appears to yield reasonable results in this situation of a
very large number of sequences with a very small number of changes, where more complex models
may be subject to overfitting. When multiple most-parsimonious trees are found, only the shortest of
these (under a p-distance criterion) is retained. Distance scoring is performed with PAUP* (Swofford,
2003). B. Table indicating the global frequency of amino acid variants in sites of interest, and the
place where they are most commonly found. Such information could be useful if a vaccine or antibody
is intended for use in a geographic region with a commonly circulating variant, so it could be
experimentally evaluated prior to testing the planned intervention. C. Examples of exploratory plots
showing A829T in Thailand and D839Y in New Zealand; such plots for any variant in any region can
be readily created at cov.lanl.gov to enable monitoring local frequency changes. D. Contiguous
regions of relatively high entropy in the Spike alignment, indicative of local clusters of amino acid
variation in the protein. The fusion-peptide cluster is used as example. It spans two sites of interest,
labeled in blue and purple in (B) and (C). The alignment in (D) is created with AnalyzeAlign.
Contemporary versions of these figures can be created at cov.lanl.gov. Care should be taken to try to
avoid systematic sequencing errors and processing artefacts among rare variants (for example, see
Fig. S7)
(De Maio et al., 2020; Freeman et al., 2020).
Supplemental Figure Titles and Legends
Fig. S1. Schematic of SARS-CoV-2 outbreak sequence processing pipeline. Related to Figure
1. The intent of these procedures is to generate, for each of several regions, a set of contiguous
codon-aligned sequences, complete in that region, without extensive uncalled bases, large gaps, or
regions that are unalignable or highly divergent, in reasonable running time for n > 30,000 ~30kb viral

18
genomes. This allows daily processing of GISAID data to enable us to track mutations. This process
provides the foundational data to enable the generation of Figs. 1-3 and 7.
A. Processing procedures:
1. Download all SARS-CoV-2 sequences from GISAID.org (34,607 as of 2020-05-29). The
downloaded sequences are stored in compressed form (via bzip2:
https://sourceware.org/git/bzip2.git).
2. Align sequences to the SARS-CoV-2 reference sequence (NC_045512), trim to desired endpoints,
and filter for coverage and quality. These steps are incorporated in a single Perl script,
'align_to_ref.pl', briefly summarized here: sequences are compressed for identity, then mapped
against the given reference sequence using 'nucmer' from the 'MUMmer' package (Kurtz et al., 2004).
The nucmer 'delta' file contains locations of matching regions and is parsed and used to, first,
partition the sequences into "good" and "bad" subsets, and then to generate alignments from the
"good" sequences.
B. Categories of sequences included and excluded from our automated alignments. A series of
criteria is used successively to exclude sequences with large internal gaps, excessive five- and three-
prime gaps, large numbers of mismatches or ambiguities (>30) overall, or regions with a high
concentration of mismatches or ambiguities (>10 in any 100 nt subsequence): the counts of these
categories of "bad" sequence are shown, for different regional genome alignments.
We then create the following different regional subalignments: CODING-REGIONS
2
(“FULL”, from the
5’-most start-codon (orf1ab) to the 3’-most stop-codon (ORF10), NC_045512 bases 266-29,674;
SPIKE, the complete surface glycoprotein coding region, bases 21,563-25,384; NEAR-COMPLETE
(“NEARCOMP”, the most-commonly-sequenced region of the genome, bases 55-29,836;
COMPLETE, matching the NC_045512 sequence from start up to the poly-A tail, bases 1-29,870; 5'
UTR, the five-prime untranslated region, bases 1-265 only. Generally speaking, the smaller the
region, the more sequences are included.
Sequences are trimmed to the extent of the reference (with minimum allowed gaps at 5' and 3' ends),
following which the pairwise alignments are generated from the matching regions, and a multiple
sequence alignment is constructed from the pairwise alignments.
3. De-duplicate
1
. To reduce computational demands, sequences are compressed by identity following
trimming to the desired region, by computing a hash value for each sequence (currently the SHA-1
message digest, 160 bits encoded as a 40-character hex string). To prevent the loss of parsimony-
informative characters when they occur in identical strings, however, multiple sequences are reduced
to a minimum of two occurrences.
4. Codon-align. Gaps are introduced into the entire compressed alignment so that the alignment
column containing the last base of each codon has a number divisible by three; this simplifies
processing of translations. Code for this procedure is derived from the GeneCutter tool from the LANL
HIV database (https://www.hiv.lanl.gov/content/sequence/GENE_CUTTER/cutter.html).
5. Partition (full/spike-only). For subalignments that encompass the spike protein and substantial
additional sequence, the spike region is extracted separately, to allow matched comparisons.
6. Build parsimony trees. A brief parsimony search (parsimony ratchet, with 5 replicates) is performed
with 'oblong' (Goloboff, 2014) This is intended as an efficient clustering procedure rather than an
explicit attempt to achieve an accurate phylogenetic reconstruction, but it appears to yield reasonable

19
results in this situation of a very large number of sequences with a very small number of changes,
where more complex models may be subject to overfitting. When multiple most-parsimonious trees
are found, only the shortest of these (under a p-distance criterion) is retained. Distance scoring is
performed with PAUP* (Swofford, 2003).
7. Re-duplicate (expand, i.e., uncompress). The original sequence names and occurrence counts are
restored to FastA format files and the appropriate leaf taxa added to the parsimony trees.
8. Sort alignment by tree. Sequences in the FastA files are sorted by the expanded tree, allowing
patterns of mutation to be discerned by inspection.
9. Mutations of interest can be readily tracked on the trees to resolve whether they are identified in
predominantly in single clades or distributed throughout the tree and likely to be recurring (e.g. Fig. 7
(sites of interest with low frequency amino acid substitutions) and Fig. S6 (Site 614)).
Fig. S2. The increasing frequency of the D614G variant over time in North America, Related to
Figure 1. Maps of the relative frequencies of D614 and G614 in North America in two different time
windows. B. Weekly running counts of G614 illustrating the timing of its spread in North America. This
figure complements Fig. 2 and S3, and Fig. 1 has details about how to read these figures. When a
particular stay-at-home order date was known for a state or county it is shown as a pink line, followed
by a light pink block indicating the maximum two-week incubation time. Different counties in California
had different stay-at-home order dates (Mar. 16-19) so are not highlighted, but more detail can be
seen regarding California in Fig. S4. The decline in D614 frequency often continues well after the
stay-at-home orders were initiated, and sometimes beyond the 14-day maximum incubation period,
when serial reintroduction of the G614 would be unlikely. On the right, Washington State is shown,
with details from two heavily sampled counties, Snohomish and King. Both counties had well-
established ongoing D614 epidemics when G614 variants were introduced, undoubtedly by travelers.
Washington state’s stay-at-home order was initiated March 24. At this time there were 1170
confirmed cases in King County, and 614 confirmed cases in Snohomish County. (Confirmed
COVID19 case count data from:
COVID-19 Data Repository by the Center for Systems Science and
Engineering (CSSE) at Johns Hopkins University)
. Testing was limited, and so this is lower bound on
actual cases. Of the sequences sampled by March 24, 95% from King County (153/161) and 100%
from Snohomish County (33/33) were the original D614 form (Part B, details at cov.lanl.gov). By mid-
April, D614 was rarely sampled. Whatever the geographic origin of the G614 variants that entered
these counties, and whether one or if multiple G614 variants were introduced, the rapid expansion of
G614 variants occurred in the framework of well-established local D614 variant epidemics. Santa
Clara county is one of the two exceptions to the pattern of D614 decline in Fig. 1B: details are
provided in Fig. S4A and C.
Fig. S3. The increasing frequency of the D614G variant over time in (A) Australia and (B) Asia,
Related to Figures 1 and 2. This figure complements Fig. 2 and Fig. S2, and Fig. 1 has details about
how to read these figures. The plot representing national sampling in Australia is on the left, with two
regional subsets of the data on the right. In each case a local epidemic started with the D614 variant,
and despite being well established, the G614 variant soon dominates the sampling. Only limited
recent sampling from Asia is currently available in GISAID; to include more samples on the map the
10-day period between March 11-20, is shown rather than the period between March 21-30; even the
limited sampling mid-March the supports the repeated pattern of a shift to G614. The Asian epidemic
was overwhelmingly D614 through February, and despite this, G614 repeatedly becomes prominent
in sampling by mid-March
.

20
Fig. S4. Two exceptions to the pattern of increasing frequency of the G614 variant over time,
from Fig. 1B, Related to Figure 1. A. Details regarding Santa Clara county, the only exceptional
pattern at the county/city level in Fig. 1B. Many samples from the Santa Clara County Department
of Public Heath (DPH) were obtained from March into May, and D614 has steadily dominated the
local epidemic among those samples. The subset of Santa Clara county samples specifically labeled
“Stanford”, however, were sampled over a few weeks mid-March through early April, and have a
mixture of both the G614 and D614 forms. These distinct patterns suggest relatively little mixing
between the two local epidemics. Why Santa Clara county DPH samples should maintain the original
form is unknown, but one possibility is that they may represent a relatively isolated community that
had limited exposure to the G614 form, and G614 may not have had the opportunity to become
established in this community – though this may be changing, see Part C. The local stay-at-home
orders were initiated relatively early, March 16, 2020. B. Details regarding Iceland, the only country
with an exceptional pattern from Fig. 1B. All Icelandic samples are from Reykjavik, and only G614
variants were initially observed there, with a modest but stable introduction of the original form D614
in mid-March. This atypical pattern might be explained by local sampling. The Icelanders conducted a
detailed study of their early epidemic (Gudbjartsson et al., 2020), and all early March samples were
collected from high risk travelers from Europe and people in contact with people who were ill; the
majority of the traveler samples from early March were from people coming in from Italy and Austria,
and G614 dominated both regions. On March 13, they began to sequence samples from local
population screening, and on March 15, more travelers from the UK and USA with mixed G614/D614
infections began to be sampled in the high-risk group, and those events were coincident with the
appearance of D614. C. Updated data regarding California from the June 19, 2020 GISAID
sampling. Most of the analysis in this paper was undertaken using the May 29, 2020 GISAID
download, but as California was an interesting outlier, and more recent sampling conducted while the
paper was under review was informative, we have included some additional plots from California data
that were available at the time of our final response to review, on June 19
th
. Informative examples
from well-sampled local regions are shown. Stay-at-home order dates are shown as a pink line,
followed by a light pink block indicating the maximum two-week incubation time. N indicates the
number of available sequences. Overall California, and specifically, San Diego and San Joaquin,
show a clear shift from D614 to G614. The transition for San Joaquin was well after the stay-at-home
orders and incubation period had passed. San Francisco shows a trend towards G614. Santa Clara
DPH, which was essentially all D614 in our May 29
th
GISAID download, had 7 G614 forms sampled in
late May that were evident in our June 19
th
GISAID download. Ventura is an example of a setting that
was essentially all G614 when it began to be sampled significantly in early April, so a transition
cannot be tracked; i.e. we cannot differentiate in such cases whether the local epidemic originated as
a G614 epidemic, or whether it went through a transition from D614 to G614 prior to sampling. The
figures in Parts A, B, and C can be recreated with more current data at https://cov.lanl.gov.
Fig. S5. Relationships among the earliest examples of the G clade and other early epidemic
samples, Related to Figures 1 and 2. Weekly running counts of the earliest forms of the virus
carrying G614. B. The GISAID G clade is based on a 4 base haplotype that distinguishes it from the
original Wuhan form. Part B shows the tallies of all of the variants among the 4 base mutations that
are the foundation of the G clade haplotype, versus the bases found in the original Wuhan reference
strain, and highlights of some of the earliest identified sequences bearing these mutations. We first
consider just the 3 mutations that are in coding regions, to enable using an alignment that contains a
larger set of sequences. One is in the RdRp protein (nucleotide C14408T resulting in a P323L amino
acid change), one in Spike (nucleotide A23403G resulting in the D614G amino acid change) and one
is silent (C3037T). The other mutation is in the 5’ UTR (C241T), and tallies based on all 4 positions
are done separately, as they are based on an alignment with fewer sequences. The earliest
examples of a partial haplotype, TTCG, are found in Germany and China (Parts A and B). This form

21
was present in Shanghai but never expanded (Part A). A cluster of infections that all carried this form
were identified in Germany, but they did not expand and were subsequently replaced with the original
Wuhan form, only to be replaced again by sequences that carry the full 4 base haplotype variant
TTTG. The first example in our alignments (see Fig. S1) found to carry the full 4 base haplotype was
sampled in Italy on Feb. 20. The first cases in Italy were of the original Wuhan form CCCA form, but
by the end of February TTTG was the only form sampled in Italy, and it is the TTTG form that has
come the dominate the pandemic. Of note, the TTCG form did not expand, and it lacked the RdRp
P323L change, raising the possibility that the P323L change may contribute to a selective advantage
of the haplotype. TTTG and CCCA are almost always linked in SARS-CoV-2 genomes, >99.9% of the
time (Part B). The number of cases where the clade haplotype is disrupted, and the form of the
disruption, are all noted in part B (not including ambiguous base calls). Some cases of a disrupted
haplotype may be due to recombination events and not de novo mutations. Given the fact that these
two forms are were co-circulating in many communities throughout the spring of 2020, and the fact
that disruptions in the 4 base pattern are rare, suggests that recombination is overall relatively rare
among pandemic sequences.
Fig. S6. Distribution of A23403G (D614G) mutation and other mutations on an approximate
phylogenetic tree using parsimony, Related to Figure 7. This tree the same as the tree shown in
Fig. 6A, but highlights complementary information: the G614 substitution, and patterns of bases that
underly the clades. It is based on the “FULL” alignment of 17,760 sequences, from the June 2
alignment, described in the pipeline in Fig. S1, from the beginning of (orf1ab) to the last stop-codon
(ORF10), NC_045512 bases 266-29,674). The outer element is a radial presentation of a full-coding-
region parsimony tree; branches are colored by the global region of origin for each virus isolate. The
inner element is a radial bar chart showing the identity of common mutations (any of the top 20
single-nucleotide mutations from the June 2 alignment), so that sectors of the tree containing a
particular mutation at high frequency are subtended by an inner colored arc; mutations not in the top
20 are presented together in gray. The tree is rooted on a reference sequence derived from the
original Wuhan isolates (GenBank accession number NC_045512), at the 3 o'clock position. Branch
ends representing sequence isolates bearing the D614G change are decorated with a gray square;
sectors of the tree containing that mutation are subtended by a dark blue arc in the inner element;
other mutations are denoted by different colors. As an example, in this tree, the region from
approximately 12:30 to 3 o'clock represents GISAID's "GR" clade, defined both by mutations we are
tracking in this paper that carry the G614 variant (the GISAID G clade, defined by mutations
A23403G, C14408T, C3037T, and a mutation in the 5’ UTR (C241T, not shown here), and an
additional 3-position polymorphism: G28881A + G28882A + G28883C. These base substitutions are
contiguous and result two amino acid changes, including N-G204R, hence GISAID’s “GR clade”
name. Close examination of this triplet in sequences from the Sheffield dataset suggests the
mutations are not a sequencing artifact. The outer phylogenetic tree was computed using oblong
(see STAR Methods), and plotted with the APE package in R. The inner element is a bar chart plotted
with polar coordinates using the gglot2 package in R. The frequency of the GR clade appears to be
increased in the UK and Europe as a subset of the regional G clade expansion, given that both carry
G614D.
Fig. S7. Investigation of S943P, Related to Figure 7. A. IGV plots showing bam files from
nanopore sequencing data of amplicons produced by the Artic network protocol. Raw data from
amplicon 81 contains a portion of adapter sequence which is homologous to the reference genome,
apart from the C variants which lead to a S943P mutation call. This region is therefore included in
variant calling if location-based trimming is not carried out. Subsequent panels show that this region
is soft clipped when trimming adapters and primers and is therefore not available for variant calling.
B. Base frequencies at position 24389 in 23 samples from the Sheffield data show that C is present in

22
half of the reads in the raw data, but is absent from trimmed and primer trimmed data. This figure is
also associated with the Star Methods.
STAR METHODS
RESOURCE AVAILABILITY
Lead Contact
Further information and requests for resources should be directed to and will be fulfilled by the Lead
Contact, Bette Korber (btk@lanl.gov).
Materials Availability
This study did not generate new unique reagents.
Data and Code Availability
All sequence data used here are available from The Global Initiative for Sharing All Influenza Data
(GISAID), at https://gisaid.org. The user agreement for GISAID does not permit redistribution of
sequences.
Web-based tools to recreate much of the analyses provided in this paper but based on contemporary
GISAID data downloads are available at cov.lanl.gov.
Code to create the alignments as described in Fig. S1 and to perform the Isotonic regression analysis in
Fig. 3 are available at GitHub.
Additional Supplemental Items are available from Mendeley Data.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Human Subjects
999 individuals presenting with active COVID-19 disease were sampled for SARS CoV-2 sequencing at
Sheffield Teaching Hospitals NHS Foundation Trust, UK using samples collected for routine clinical
diagnostic use. This work was performed under approval by the Public Health England Research Ethics
and Governance Group for the COVID-19 Genomics UK consortium (R&D NR0195). SARS-CoV-2
sequences were generated using samples taken for routine clinical diagnostic use from 999 individuals
presenting with active COVID-19 disease: 593 female, 399 male, 6 no gender specified; ages 15-103
(median 55) years.
METHOD DETAILS
DETECTION AND SEQUENCING OF SARS-CoV-2 ISOLATES FROM CLINICAL SAMPLES
Samples for PCR detection of SARS-CoV-2 (Fig. 5A) were all obtained from either throat or combined
nose/throat swabs. Nucleic acid was extracted from 200µl of sample on MagnaPure96 extraction

23
platform (Roche Diagnostics Ltd, Burgess Hill, UK). SARS-CoV-2 RNA was detected using primers
and probes targeting the E gene and the RdRp genes for routine clinical diagnostic purposes, with
thermocycling and fluorescence detection on ABI Thermal Cycler (Applied Biosystems, Foster City,
United States) using previously described primer and probe sets (Corman et al., 2020). Nucleic acid
from positive cases underwent long-read whole genome sequencing (Oxford Nanopore Technologies
(ONT), Oxford, UK) using the ARTIC network protocol (accessed the 19
th
of April,
https://artic.network/ncov-2019.) Following base calling, data were demultiplexed using ONT Guppy
using a high accuracy model. Reads were filtered based on quality and length (400 to 700bp), then
mapped to the Wuhan reference genome and primer sites trimmed. Reads were then downsampled
to 200x coverage in each direction. Variants were called using nanopolish
(https://github.com/jts/nanopolish) and used to determine changes from the reference. Consensus
sequences were constructed using reference and variants called.
PSEUDOTYPED VIRUS INFECTIVITY
VSV System
Plasmids for full-length SARS-Cov-2 Spike were generated from synthetic codon-optimized DNA
(Wuhan-Hu-1 isolate, GenBank: MN908947.3) through sub-cloning into the pHCMV3 expression
vector, with a stop codon included prior to the HA tag. The D614G variant was generated by site-
directed mutagenesis. Positive clones were fully sequenced to ensure that no additional mutations
were introduced.
Lentiviruses for stable cell line production were generated by seeding 293T cells at a density of 1x10
6
cells/well in a 6-well dish. Once the cells reached confluency, they were transfected with 2ug pCaggs-
VSV-G, 2ug of lentiviral packaging vector pSPAX2, and 2ug of lentiviral expression plasmid pCW62
encoding ACE2-V5 and the puromycin resistance gene (pCW62-ACE2.V5-PuroR) or TMPRSS2-
FLAG and the blasticidin resistance gene (pCW62-TMPRSS2.FLAG-BlastR) using Trans-IT
transfection reagent according to manufacturer’s instructions. 24 hours post-transfection, media was
replaced with fresh DMEM containing 10% FBS and 20mM HEPES. 48 hours post-transfection,
supernatants were collected and filtered using a 0.45um syringe filter (VWR Catalog #28200-026).
293T-ACE2 cells were generated by seeding 293T cells at a density of 1x10
6
cells/well in a 6-well
dish. At confluency, cells were transduced with 100uL of ACE2.V5-PuroR lentivirus. 48 hours post-
transduction, cells were placed under 5ug/ml puromycin. 293T-ACE2+TMPRSS2 cells were
generated by seeding 293T-ACE2 cells at a density of 1x10
6
cells/well in a 6-well dish. At confluency,
cells were transduced with 100uL of TMPRSS2.FLAG-BlastR lentivirus. 48 hours post-transduction,
cells were placed under 10ug/ml blasticidin selection.
Recombinant SARS-CoV-2-pseduotyped VSV-∆G-GFP were generated by transfecting 293T cells
with phCMV3 expressing the indicated version of codon-optimized SARS-CoV-2 Spike using TransIT
according to the manufacturer's instructions. At 24 hr post-transfection, the medium was removed,
and cells were infected with rVSV-G pseudotyped ∆G-GFP parent virus (VSV-G*∆G-GFP) at MOI = 2
for 2 hours with rocking. The virus was then removed, and the cells were washed twice with OPTI-
MEM containing 2% FBS (OPTI-2) before fresh OPTI-2 was added. Supernatants containing rVSV-
SARS-2 were removed 24 hours post-infection and clarified by centrifugation.
Viral titrations were performed by seeding cells in 96-well plates at a density sufficient to produce a
monolayer at the time of infection. Then, 10-fold serial dilutions of pseudovirus were made and added
to cells in triplicate wells. Infection was allowed to proceed for 12-16 hr at 37 ˚C. The cells were then
fixed with 4% PFA, washed two times with 1xPBS and stained with Hoescht (1ug/mL in PBS). After

24
two additional washes with PBS, pseudovirus titers were quantified as the number of fluorescent
forming units (ffu/mL) using a CellInsight CX5 imager (ThermoScientific) and automated enumeration
of cells expressing GFP.
Lentiviral System
Additional assessments of corresponding D614 and G614 Spike pseudotyped viruses were
performed by using lentiviral vectors and infection in 293T/ACE2.MF and TZM-bl/ACE2.MF cells
(both cell lines kindly provided by Drs. Mike Farzan and Huihui Mu at Scripps). Cells were maintained
in DMEM containing 10% FBS, 1% Pen Strep and 3 ug/ml puromycin. An expression plasmid
encoding codon-optimized full-length spike of the Wuhan-1 strain (VRC7480), was provided by Drs.
Barney Graham and Kizzmekia Corbett at the Vaccine Research Center, National Institutes of Health
(USA). The D614G amino acid change was introduced into VRC7480 by site-directed mutagenesis
using the QuikChange Lightning Site-Directed Mutagenesis Kit from Agilent Technologies (Catalog #
210518). The mutation was confirmed by full-length spike gene sequencing. Pseudovirions were
produced in HEK 293T/17 cells (ATCC cat. no. CRL-11268) by transfection using Fugene 6
(Promega Cat#E2692). Pseudovirions for 293T/ACE2 infection were produced by co-transfection with
a lentiviral backbone (pCMV ∆R8.2) and firefly luciferase reporter gene (pHR' CMV Luc)
(Naldini et
al., 1996). Pseudovirions for TZM-bl/ACE2 infection were produced by co-transfection with the Env-
deficient lentiviral backbone pSG3∆Env (kindly provided by Drs Beatrice Hahn and Feng Gao).
Culture supernatants from transfections were clarified of cells by low-speed centrifugation and
filtration (0.45 µm filter) and used immediately for infection in 96-well culture plates. 293T/ACE2.MF
cells were preseeded at 5,000 cells per well in 96-well black/white culture plates (Perkin-Elmer
Catalog # 6005060) one day prior to infection. Sixteen wells were inoculated with 50 ul of a 1:10-
dilution of each pseudovirus and incubated for three days. Luminescence was measured using the
Promega Luciferase Assay System (Catalog # E1501). For infection of TZM-bl/ACE2.MF cells,
10,000 freshly trypsinized cells were added to 16 wells of a 96-well clear culture plate (Fisher
Scientific) and inoculated with undiluted pseudovirus. Luminescence was measured after 2 days in a
solid black plate using the Britelite Plus Reporter Gene Assay System (Perkin-Elmer). Luminescence
in both assays was measured using a PerkinElmer Life Sciences, Model Victor2 luminometer. HIV-1
p24 content (produced by the backbone vectors) was quantified using the Alliance p24 ELISA Kit
(PerkinElmer Health Sciences, Cat# NEK050B001KT). Reported relative luminescence units (RLUs)
were adjusted for p24 content.
Neutralization Assay
Pre-titrated amounts of rVSV-SARS-CoV-2 (D614 or G614 variant) were incubated with serially
diluted human sera at 37 °C for 1 hr before addition to confluent Vero monolayers in 96-well plates.
Infection proceeded for 12-16 hrs at 37 °C in 5% CO
2
before cells were fixed in 4% paraformaldehyde
and stained with 1ug/mL Hoescht. Cells were imaged using a CellInsight CX5 imager and infection
was quantitated by automated enumeration of total cells and those expressing GFP. Infection was
normalized to the percent cells infected with rVSV-SARS-CoV-2 incubated with normal human sera.
Data are presented as the relative neutralization for each concentration of sera.
DATA PIPELINE
Background and General Approach
The Global Initiative for Sharing All Influenza Data (GISAID) (Elbe and Buckland-Merrett, 2017; Shu
and McCauley, 2017) has been coordinating SARS-CoV-2 genome sequence submissions and
making data available for download since early in the pandemic. At time of this writing, hundreds of
sequences were being added every day. These sequences result from extraordinary efforts by a wide
variety of institutions and individuals: while an invaluable resource, but are mixed in quality. The

25
complete sequence download includes a large number of partial sequences, with variable coverage,
and extensive 'N' runs in many sequences. To assemble a high-quality dataset for mutational
analysis, we constructed a data pipeline using some off-the-shelf bioinformatic tools and a small
amount of custom code.
From theSARS-CoV-2 sequences available from GISAID, we derived a "clean" codon-aligned dataset
comprising near-complete viral genomes, without large insertions or deletions ("indels") or runs of
undetermined or ambiguous bases. For convenience in mutation assessment, we generated a codon-
based nucleotide multiple sequence alignment, and extracted translations of each reading frame,
from which we generated lists of mutations. The cleaning process was in general a process of
deletion, with alignment of retained sequences; the following criteria were used to exclude
sequences:
1. Fragmented matching (> 20 nt gap in match to reference)
2. Gaps at 5' or 3' end (> 3 nt)
3. High numbers of mismatched nucleotides (> 20), 'N' or other ambiguous IUPAC codes.
4. Regions with concentrated ambiguity calls: >10 in any 50 nt window)
Any sequence matching any of the above criteria was excluded in its entirety.
Sequence Mapping and Alignment
Sequences were mapped to a reference (bases 266:29674 of GenBank entry NC_045512; i.e., the
first base of the ORF1ab start codon to the last base of the ORF10 stop codon) using "nucmer" from
the MUMmer package (version 3.23; (Kurtz et al., 2004)). The nucmer output "delta" file was parsed
directly using custom Perl code to partition sequences into the various exclusion categories
(Sequence Mapping Table) and to construct a multiple sequence alignment (MSA). The MSA was
refined using code derived from the Los Alamos HIV database "Gene Cutter" tool code base. At this
stage, alignment columns comprising an insertion of a single "N" in a single sequence (generating a
frame-shift) were deleted, and gaps were shifted to conform with codon boundaries.
Using the initial "good-sequence" alignment, a low-effort parsimony tree was constructed. Initially,
trees were built using PAUP* (Swofford, 2003) with a single replicate heuristic search using stepwise
random sequence addition; subsequently, a parsimony ratchet was added; currently, oblong
(Goloboff, 2014) is used. Sequences in the alignment were sorted vertically to correspond to the
(ladderized) tree, and reference-sequence reading frames were added. See Fig. S1 for a pipeline
schematic.
Data partitioning and phylogenetic trees
Alignments were made and trees inferred for three distinct data partitions, the longer the alignments,
the fewer sequences the sequences (Fig. S1.) The full genome tree was used for Fig. 7. Trees were
inferred by either of two methods: 1. neighbor-joining using a p-distance criterion, (Swofford, 2003) or
2. parsimony heuristic search using a version of the parsimony ratchet (Goloboff, 2014), the general
conclusions in Fig. 7 were substantiated in both; the parsimony tree is shown.
Global Maps
The Covid-19 pie chart map is generated by overlaying Leaflet (a JavaScript library for interactive
maps) pie charts on maps provided by OpenStreetMap. The interface is presented using
rocker/shiny, a Docker for Shiny Server.
QUANTIFICATION AND STATISTICAL ANALYSIS

26
SYSTEMATIC REGIONAL ANALYSIS OF D614/G614 FREQUENCIES
To observe a significant change in the frequency of two SARS-CoV-2 variants in a geographic region,
three minimal requirements must be met. Both variants must have been introduced into an area and
be co-circulating, data must be sampled for a long enough period to observe a change in frequency,
and there must be enough data to be powered adequately to detect a difference.
We use the bioinformatic approaches described above to extract from GISAID all the politically
defined geographic regions within the data that met these criteria, to track changes in frequency in a
systematic way using all available data. The political/geographical regions we use are strictly
hierarchically segmented based on the naming conventions used in GISAID. GISAID data is labeled
such that the geographic source is noted first as a continent or Oceania; we call this Level 1. Level 2
is the country of origin of a sample. Level 3 are subcountries and states, and although occasionally
level 3 includes a major city in a small country. For this purpose, England, Scotland, and Wales are
considered sub-countries of the United Kingdom, and assigned level 3; the sampling in the UK has
been the most extensive globally to date. Level 4, is the county or city of origin. The levels are strictly
hierarchical, and within a given level, the geographical regions do not overlap. In some cases (e.g.,
Nepal_Kathmandu and Nepal, Greece_Athens and Greece, Italy_Veneto_Verona and Italy_Veneto,
or Iceland_Reykjavik and Iceland) the sampling in a sub-level exactly matches the sampling in the
corresponding upper level, in which case the sub-level is not presented. Levels 3 and 4 are not
always available, and the day of sampling is also not always available.
The statistical strategies we use are then applied separately in each country, region or city, and we
do not assume that outbreaks in each political subdivision are independent and identically
distributed. Instead, our model assumption is that the individuals we test within a region are
independent. This assumption may fail if there are sampling biases in a region that change over a
given period of time. The G614 form is part of the G clade haplotype that is introduced by travelers,
as we discuss in the text, and it is rare for it to arise independently. Our null hypothesis is that the
observed shifts in frequency are random nondirectional drift. We have taken two statistical
approaches to test this.
Fisher’s exact comparison
For this comparison, we used a two-sided Fisher's exact test to compare the G614 and D614 counts
in the pre-onset and the post-delay periods, as described in the text, and provides a p-value against
the null hypothesis that the fraction of D614 and G614 sequences did not change. To be included in
the analysis, 15 sequences were required pre-onset, with a mixture of D614 and G614 present such
that the rarer form was present at least 3 times; we also required a minimum of 15 sequences be
sample at least 2 weeks later, to create a post-delay set. Only regions for which p<0.05 are
considered, based on a two sided-test. We then use a binomial test to evaluate the null hypothesis
that in regions where we saw significant change in sampling frequency over time, the shift was as
likely to be an increase or a decrease in G614 across geographic regions. This analysis is presented
in Fig. 1B.
Isotonic Regression
Isotonic regression forms the basis of a one-sided test of the hypothesis for positive selection based
on fitting the indicator that the typed strain is G as a logistic regression in which the logarithm of the
odds ratio is a non-decreasing function of time. We use the residual deviance of the fitted model as
our test statistics. To be included in this analysis, a region was required to have at least 5 sequences
each of D614 and G614, and a minimum of 14 sampling days of data available. While we have a

27
composite null hypothesis (the log-odds ratio is non-increasing), assuming that the log-odds ratio
remains constant over time leads to tests that have largest power. While the classical chi-square
approximation does not hold, we can sample from the constant log-odds ratio by permuting the vector
of variant labels, and refitting the isotonic logistic regression. We performed 400 randomizations of
the data in each region. Hence the lowest p-value we can obtain is 0.0025. The reverse hypothesis,
namely than the fraction of G variant decreases with time is also tested by fitting a non-increasing
function of time. The isotonic logistic regression was done using R and the cgam package. We
applied the bionomial test across regions with a significant change in one direction, as we did for the
Fishers test results. This analysis is presented in Fig 3 and Data S1.
CLINICAL DATA AND MODELING
Baseline Comparisons of Clinical Parameters
Univariate analysis showed no associations between the age of individuals and their D614 (median
54.8, IR 39.4-77) or G614 status (median 54.6 (38.7-72.8) (Wilcoxon rank sum p = 0.37), nor with
D614 and G614 and sex (Fisher’s exact p=0.32). Comparing hospitalization and age, the median (IR)
are: for all hospitalized, (IP+OCU), 74 years (59-83); for all OP, 44 (32-54), Wilcoxon p < 2.2e-16.
67% of males were hospitalized, versus 33% of females (Fisher’s exact p = p-value < 2.2e-16).
Modeling PCR Ct
Two PCR Ct methods were used as a surrogate for estimating in vivo viral load in the upper
respiratory tract, switching methods in mid-April due a shortage of kits. The first method involved
nucleic acid extraction; the second method, heat treatment (Fomsgaard and Rosenstierne, 2020).
To assess the impact of available clinical parameters on viral load as measured by PCR Cts, we used
a linear model, predicting Ct from PCR method, Sex, Age and D614G variant. This revealed that only
the PCR method and the D614G variant were statistically significant. A negative coefficient for the G
variant indicated that patients infected by the latter have, on average, a higher viral load, but that that
viral load is not impacted by neither age nor sex.
The results from the smaller model are:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.301 0.3166 76.757 <2e-16 ***
G614 -0.7763 0.3718 -2.088 0.037 *
Method_2 3.1979 0.3658 8.743 <2e-16
Results comparing D614G status for the two methods were also evaluated independently, and the
first method showed a significant association between lower Ct values and presence of G614
(Wilcoxon p = 0.033), but the second method, with many fewer samples, did not reach significance.
Predicting Hospitalization
The simple Fisher’s exact test analysis in Fig. 5 indicates that the D614G status is not predictive of
hospitalization, even though it is predictive of viral load. We can make a first analysis to predict
hospitalization from viral load, gender, age and D614G status:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.548823 0.624270 -12.092 < 2e-16 ***
G614 0.112038 0.214107 0.523 0.600779

28
Male 1.490789 0.181695 8.205 2.31e-16 ***
Age 0.089444 0.005664 15.791 < 2e-16 ***
CT 0.069376 0.018243 3.803 0.000143 ***
Method_2 -0.358397 0.218856 -1.638 0.101506
As somewhat expected, the D614G status is not statistically significant, even though viral load is, but
the coefficient goes in the opposite direction than we would have intuited: a lower viral load is
predictive of a higher probability of hospitalization. Sex (Male) and Age both increase the probability
of hospitalization.
Predicting Hospitalization, revisited
Although the above analysis indicates that aa614G does not predict hospitalization directly, it does
predict viral load and viral load predicts hospitalization; so there is a concern that aa614G might
affect hospitalization, but that this effect is “masked” by the viral load. To explore this hypothesis, we
“unmask” the aa614G by using the residuals from the regression of Ct on extraction method and
D614G status to get a second predictive model for hospitalization:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.889991 0.393950 -14.951 < 2e-16 ***
G614 0.029858 0.209349 0.143 0.886587
Corrected Ct 0.069276 0.018225 3.801 0.000144 ***
Male 1.490690 0.181584 8.209 2.22e-16 ***
Age 0.089714 0.005661 15.849 < 2e-16 ***
In these regression analyses, the estimated coefficients for age, sex and viral load (corrected or not
for method and strain) remain mostly unchanged, and strain still does not have an effect.
All other comparisons were not significant. All coding was done using R. Results of these analysis are
presented in the main text and in Fig. 5.
Modeling pseudotype virus infectivity
We used a log-normal generalized linear model (GLM) to test whether the G614 variant grew to
higher titers than the wildtype D614 virus in Vero, 293T-ACE2 and 293T-ACE2-TMPRSS2 cell lines.
The full experiment was repeated twice, each time in triplicate, and the 2 experimental repeats were
considered random effects. Viral variant and cell line were considered as fixed effects. On average,
across all cell lines, G614 grows to about a 3-fold (2.95) higher titer than D614 (p=9x10
-11
). A
significant interaction was found between viral variant and cell line (p=0.002), indicating that the
relative increase of G614 compared to D614 was significantly different across cell lines (p=0.002).
Results of these analysis are presented in Fig. 6A.
Sequence quality control
We discovered a sequencing processing error that gave rise to what appeared at first to be a
mutation of interest at position 943 (24389 A>C and 24390 C>G) in Spike that was evident in
sequences from Belgium. It was frequent enough to be a site of interest, and was tracked. We
contacted the group in Belgium, the source of the data, who were already aware of the issue,
concurred with our interpretation, and they had been in touch with GISAID with a request to remove
the problematic sequences.

29
We identified the issue with this site as part of another study using a method to detect systematic
sequencing errors (Freeman et al., 2020); we are interrogating the quality of available sequencing
data and these positions were highlighted as suspect. We interrogated these positions in the raw
sequencing data from Sheffield, and although these two variants are not present in the final
consensus sequence from any of the Sheffield isolates, the raw, untrimmed bam files show their
presence in only one of the amplicons covering the site (Fig. S7 A&B). We noticed that in fact this
position is to the left of the 5' primer of amplicon 81 in what we believe to be an adapter sequence.
Comparison of the Wuhan reference and the adapter sequence reveals similarity around this position:
Nanopore adapter sequence:
CAGCACCTT
The Wuhan reference sequence:
CAGCAAGTT
In our validation set, we see a C present at around 50% of called bases at both these positions in raw
data but this region is trimmed by the ARTIC pipeline and is therefore not used to call variants and
contribute to the final consensus sequence. Although it is evident in amplicon 81, in this region, there
is no evidence for these variants in the data from amplicon 80, which also covers these positions. We
include a figure (Fig. S7) to explain our finding.
In summary this is an error that has arisen due to a combination of improper trimming of adapter and
primer regions from raw sequencing reads before downstream analysis, and the coincidental
homology between the nanopore adapter sequence and the Wuhan reference genome in this region.
This is included here as a cautionary note; resolving rare biological mutations and sequencing error
will be an important balance going forward in terms of interpretation of rare mutations
(De Maio et al.,
2020). A recurrent amino acid change like L5F (Fig. 7) could potentially result from a recurrent
sequencing or sequence processing error (De Maio et al., 2020), or alternatively, it may be of
particular interest if it is naturally recurring homoplasy.
ADDITIONAL RESOURCES
Current data updates, analytical results, and webtools: cov.lanl.gov
Supplemental Tables and Legends
Table S1. GISAID acknowledgments, Related to Figures 1-3, and 7.
Table S2. Summary of Spike Variation as of May 29, 2020, Related to Figure 7. This is a
spreadsheet with tabs including the following data: i) Sites of interest: A listing of all sequences with
amino acid changes in sites of interest in Spike (those that reach >0.3% of the global population, or if
in ACE2 contact regions, >0.1% of the population). ii) D614G: A separate tab for D614G variants. iii)
Spike Variation: A tally of all amino acid variants and the overall entropy for each position in Spike,
as well as the entropy for each 10 amino acid contiguous stretch across the protein, is listed
(https://www.hiv.lanl.gov/content/sequence/ENTROPY/entropy.html). This table includes annotation
of the RDB location and ACE2 contact sites. Regular updates of all information in Table S2 can be
found at https://cov.lanl.gov.

30
Supplemental Data
Data S1. Modeling the daily fraction of the G614 variant as a function of time in local regions
using isotonic regression, Related to Figure 3.
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Highlights:
-
A SARS-CoV-2 variant with Spike G614 has replaced D614 as the dominant pandemic
form
-
The consistent increase of G614 at regional levels may indicate a fitness advantage
-
G614 is associated with lower RT PCR Ct’s, suggestive of higher viral loads in patients
-
The G614 variant grows to higher titers as pseudotyped virions
In Brief
Korber et al. present evidence that there are now more
SARS-CoV-2 viruses circulating the in
the human population globally that have the G614 form of the Spike protein versus the D614
form that was originally identified from the first human cases in Wuhan, China. Follow-up
studies show that patients infected with G614 shed more viral nucleic acid compared to those
with D614, and G614-bearing viruses show significantly higher infectious titers in vitro than their
D614 counterparts.

A
B
Prior to March 1 March 21-30
Date 2020
C
Weekly running counts
Date 2020
Level 3: Subcounty/State, 16/16, p = 3.1e-05 Level 4: County/City, 11/12, p = 0.0063
Fraction D614 vs G614
Fraction D614 vs G614
S D614
S G614
Level 1: Continent, 5/5, p = 0.0625 Level 2: Country, 16/17, p = 0.00027

A
S D614
S G614
Prior to March 1 March 21-30
B
Weekly running counts
Date 2020 Date 2020

GISAID level 3: Subcounty/State GISAID level 4: County/City
Level 3: County/City
#
D614
#
G614
# of
days
Time
window
days
G614
increasing p-
value
G614
decre asing
p-v alue
Australi a_New -South-W ales _Sy dney 189 179 51 90 0.00025 1.00
Spain_Comunitat-Valenciana_Valencia 72 97 30 34 0.00025 0.64
United-Kingdom_England_Bristol 240 629 35 37 0.00025 0.28
United-Kingdom_England_Cambridge 751 3020 81 81 0.00025 1.00
United-Kingdom_England_Liverpool 97 484 46 45 0.00025 0.71
United-Kingdom_England_Nottingham 204 386 67 76 0.00025 0.99
United-Kingdom_England_Sheffield 120 431 44 51 0.00025 1.00
USA_Washington_King 173 75 58 69 0.00025 0.99
USA_Washington_Pierce 32 35 21 38 0.00025 1.00
USA_Washington_Snohomish 35 32 27 93 0.00025 1.00
USA_Wisconsin_Milwaukee 66 30 32 45 0.00025 0.97
United-Kingdom_England_Norwich 29 269 26 28 0.00075 0.97
USA_California_San-Diego 11 75 33 58 0.002 0.95
United-Kingdom_England_London 36 357 19 24 0.0085 0.91
USA_Wisconsin_Madison 13 43 26 35 0.030 0.39
USA_New-York_Manhattan 38 339 30 45 0.036 0.90
USA_California_San-Francisco 59 83 21 48 0.049 0.34
USA_New-York_Brooklyn 13 292 31 46 0.070 0.87
USA_Washington_Yakima 184 59 31 36 0.073 0.00025
USA_California_Santa-Clara 165 24 50 76 0.49 0.00025
Of 19 cit ies with a clea r direction, 17 a re increasing: Binomial p = 0. 0007
Sydney Cambridge Yakima
Fraction of G614
Feb 01 Mar 01 Apr 01 Mar 01 Apr 01 May 01 Mar 16 Mar 30 Apr 13
Date 2020 Date 2020Date 2020
Weekly running counts
Level 2: Reg ion
#
D614
#
G614
# of
days
Time
window
days
G614
increasing p-
value
G614
decrea sing
p-v alue
Australia _New- South-W ales 189 180 51 90 0.00025 0.99
Australia _Victori a 226 306 43 80 0.00025 0.93
Belgium_Ghent 12 37 18 26 0.00025 0.77
China_B eijing 46 25 35 66 0.00025 0.98
Germany_North-Rhine-Westphalia 16 37 16 27 0.00025 0.91
Spain_Comunitat-Valenciana 73 110 32 34 0. 00025 0.95
Taiwan_Taoyuan 14 12 16 85 0.00025 0.51
United-Kingdom_England 1904 6338 88 109 0.00025 1.00
United-Kingdom_Scotland 433 1125 73 75 0.00025 0.68
United-Kingdom_Wales 717 1792 45 54 0.00025 0.95
USA_Arizona 11 64 22 71 0.00025 0.99
USA_California 267 199 70 111 0.00025 0.001*
*USA_California excluding Santa Clara 102 175 53 107 0.00025 0.99
USA_Michigan 31 382 38 53 0.00025 0.85
USA_New-York 91 1163 52 55 0.00025 1.00
USA_Utah 24 253 38 45 0.00025 0.98
USA_Virginia 27 220 43 47 0.00025 0.99
USA_Washington 926 683 69 101 0.00025 1.00
USA_Wisconsin 156 197 49 93 0.00025 0.07
India_Maharashtra 30 35 30 48 0.0005 0.97
Thailand_Bangkok 26 12 19 70 0.0005 0.72
Chile_S antia go 19 63 27 34 0.00075 0.99
USA_Illinois 38 56 26 88 0.00075 0.98
United-Kingdom_Northern-Ireland 47 60 22 27 0.00325 0.42
Australia _South-Aus tralia 15 58 23 32 0.0035 0.84
USA_Minnesota 51 96 35 47 0.004 0.76
Taiwan_Taipei 17 26 26 94 0.0053 0.45
Australia _Queensl and 12 13 14 58 0.0058 0.96
USA_Florida 11 35 18 69 0.009 0.82
USA_Connecticut 22 127 34 71 0.0095 0.93
Belgium_Liege 19 140 24 44 0.016 0.47
Denmark_Unknown 32 493 34 34 0.026 0. 56
Canada_O ntario 26 32 15 57 0.062 0.91
India_Gujarat 10 110 22 36 0.085 0.08
Austria_V ienna 10 108 33 41 0.12 0.53
Spain_Madrid 20 56 16 33 0.15 0.89
Netherlands_Noord-Brabant 44 32 18 23 0.21 0.64
USA_Texas 75 169 29 53 0.26 0.72
Netherlands_Zuid-Holland 15 75 23 29 0.28 0.69
Netherlands_Utrecht 18 52 31 55 0.30 0.54
India_Delhi 24 13 16 32 0.93 0.00075
Of 31 reg ions with a clea r direction, 30 ar e increasing: Binomial p = 2. 98e-08
A
B

VERO 293T-ACE2 293T-ACE2
+TMPRSS2
105
106
107
ffu/mL
rVSV-SARS-CoV-2
D614
G614
D614
G614
Bkg
0
50000
100000
150000
200000
250000
RLU
SARS-CoV-2pp, 293T/ACE2
D614
G614
Bkg
0
10000
20000
30000
40000
RLU
SARS-CoV-2pp, TZM-bl/ACE2
D
15 20 25 30 35 40
Cycle Threshold for Diagnostic PCR
(Lower values indicate higher viral loads)
G614 G614D614D614
Method 1 Method 2
PCR cycles
A
Hospitalization: D614 vs G614 Fisher’s exact p = 0.66
Outpatient
Inpatient
ICU
D614
0
5
10
15
20
25
count
G614
25 50 75 100 25 50 75 100 25 50 75 100
0
5
10
15
20
25
Age
count
127 110
410 307 39
5
B
GF
C
I
CT D614 vs G614, GLM p = 0.037
VERO 293T-ACE2 293T-ACE2
+TMPRSS2
105
106
107
ffu/mL
rVSV-SARS-CoV-2
D614
G614
50
S0
S2
S2’
150
100
75
37
kDa
D614
G614
VSV-G
Empty
250
Actin
D614
G614
VSV-G
Empty
250
100
75
150
50
25
kDa
S2
VSV-M
H
E
**** **** **** **** ****
% Relative Infection
Log dilution Log dilution
Log dilution
PCR Method 1: NA extract. PCR Method 2: Heat treat.
A B
OP IP ICU
D614 127 110 5
G614 410 307 39
Fisher’s exact, 2x2: (OP+IP) x ICU = 0.047
Fisher’s exact, 2x2, OP x (IP+ICU): p = 0.66
15 20 25 30 35 40
Cycle Threshold for Diagnostic PCR
(Lower values indicate higher viral loads)
G614 G614D614D614
Method 1 Method 2
PCR cycles
PCR Method 2:
Heat treat
Ct D614 vs G614
GLM p = 0.037
Hospitalization D614 vs G614
Fisher’ p = 0.66
Cycle Threshold (Ct) for Diagnostic PCR
(Lower values indicate higher viral loads)
Outpatient Inpatient ICU
D614
G614
Count
Count
PCR Ct
PCR Method 1:
NA extract

VERO 293T-ACE2 293T-ACE2
+TMPRSS2
105
106
107
ffu/mL
rVSV-SARS-CoV-2
D614
G614
D614
G614
Bkg
0
50000
100000
150000
200000
250000
RLU
SARS-CoV-2pp, 293T/ACE2
D614
G614
Bkg
0
10000
20000
30000
40000
RLU
SARS-CoV-2pp, TZM-bl/ACE2
B
A
D
VERO 293T-ACE2 293T-ACE2
+TMPRSS2
105
106
107
ffu/mL
rVSV-SARS-CoV-2
D614
G614
E
C
**** **** **** **** ****
% Relative Infection
Log dilution Log dilutionLog dilution
% Relative Infection

VTLADAGFIKQYGD28856 98.78
-------------Y149 0.51 Portugal, New Zealand
---T---------- 91 0.31 Thailand
-----V-------- 32 0.11 Iceland
L------------- 18 0.06 Oxford
----------H--- 11 0.04 Cambridge
-----------H-- 7 0.02 Cambridge
--------T----- 4 0.01 UK
----------L--- 3 0.01 Virginia
Spike Spike Region N=28,637 Geographic
Mutation Possible Impact Count Sampling
D614G SARS-CoV epitope 20468 (71%) Global
L5F Signal Peptide 170 (0.6%) Global
R21I/K/T S1 NTD domain 133 (0.5%) Wales, England, others
A829T/S Fusion Peptide 91 (0.3%) Thailand
D839Y/N/E Fusion Peptide 149 (0.5%) Portugal, New Zealand, others
D936Y/H Heptad Repeat 1(HR1) 257 (0.9%) Sweden, UK, others
P1263L Cytoplasmic Tail 201 (0.7%) UK, others
Higher Entropy Mutational clusters Count Percent. Local cluster
2-9 Signal Peptide
18-29 N-terminal domain (NTD)
46-54 NTD
71-80 NTD
138-148 NTD
475-483 RBD
611-615 S2, Protomer interactions
826-839 Fusion Peptide
936-940: HR1
A
B
C
Monitoring
sites of
interest
in local
epidemics
Weekly running counts
Date 2020
Thailand
New Zealand
D

Download all SARS-CoV-2
sequences from GISAID Sequence filtering results
Align to reference (NC_045512):
trim to desired endpoints
filter for coverage and quality
De-duplicate1
Codon-align
Partition (full/spike-only)
Build parsimony trees
Re-duplicate
Sort alignment by tree
NEAR-
COMPLETE
14105
19607
437
1546
733
15115
270
1159
114
174
57
2
CODING
REGIONS2
17222
17172
73
142
148
15091
323
1323
48
21
0
3
SPIKE
28576
6382
160
35
12
4541
400
1065
33
11
0
122
COMPLETE
3019
28984
2321
624
9391
15132
9
57
1229
51
0
2
5' UTR
4228
27886
26733
9
125
6
6
10
72
21
5
920
category
"good"
"bad"
5' end gaps
3' end gaps
5' end gaps + 3' end gaps
fragmented matching
Excess "N" stretches
Excess mismatches (>30)
5' end gaps + mismatches
3’ end gaps + mismatches
5' + 3' end gaps +
mismatches
No matching detected
bad
good
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
1
10
100
1000
10000
1
10
100
1000
10000
ambiguities per sequence
occurrence count
Ambiguous
base calls
(primarily N)
Sequence filtering results
Sequence Processing Pipeline
Notes:
1. Multiply occurring identical sequences are reduced to 2 occurrences to so that parsimony-informative sites do not become unique.
2. "Coding regions" subset includes sequences passing error filtering, bounded from the orf1ab start codon to the ORF10 stop codon
(NC_045512 genome positions 266-29674).
A
Sequence Processing Pipeline
B

AS D614
S G614
Prior to March 1 March 21-30
B
Weekly running counts
Date 2020 Date 2020

AD614
G614
Prior to March 1 March 21-30
Australia Australia
March 11-20
Asia
Prior to March 1
Asia Asia
B
Weekly running counts
Weekly running counts
Date 2020 Date 2020

Santa Clara County Iceland
Early cases of the G clade
?
?
Nearly all G614 samples are labeled “Stanford”,
suggesting a local cluster with Santa Clara county.
?
D614 persists among
Santa Clara Dept. of
Public Health samples
Population
High Risk Screening
Targeted testing: Jan 31 – Mar 15 157 sequences
Travelers to high risk places plus people with contacts
Before the black line ~50% of samples from travelers from
Austria/Italy/Switzerland, dominated by D614
Population Screening, Mar 13 - Apr 1, 85 sequences
G614 began to be first detected in this time frame
A B
CD
9692 TTTG (71.65%)
3835 CCCA (28.35%)
51 TCTG 5 CTCG
32 TTCG 4 CCTA
13 CTTG 3 TCTA
11 TCCA 2 CTTA
9 TCCG 2 CTCA
7CCCG 1 TTCA
6 TTTA 1 CCTG
11805 TTG (72.03%)
4582 CCA (27.96%)
53 CTG
39 TCG
16 CCG
9 TTA
8 CTA
5 TCA
1 ACA
Earliest examples in GISAID:
TTCG: Germany, Jan 2020: cluster of cases late Jan.-Feb.
One example: Germany/BavPat1/EPI_ISL_406862|2020-01-28
TTCG: Sampled several times in China, e.g.:
Sichuan/SC-PHCC1-022/EPI_ISL_451345|2020-01-24
Shanghai/SH0025/EPI_ISL_416334|2020-02-06
Guangzhou/GZMU0019/EPI_ISL_429080|2020-02-05
CCCG: Sampled twice in early Feb., Wuhan and Thailand
Thailand/Samut_prakarn_840/EPI_ISL_447919|2020-02-04
Wuhan/HBCDC-HB-06/EPI_ISL_412982|2020-02-07
TTTG: First identified in Italy; within 10 days sampled in
many in countries in Europe, the USA, Mexico
First sample: Italy/CDG1/2020|EPI_ISL_412973|2020-02-20
G-clade mutations (C3037T, C14408T, A23403G) CCA -> TTG
Plus the linked mutation in the UTR: C241T CCCA -> TTTG
G614is common
only in Santa Clara
samples labeled
Stanford
Santa Clara county is
particularly well sampled
in California March
Weekly running counts
Weekly running counts
Variants:
Targeted testing: Mar 15 – Mar 31
More travelers from the USA and UK, mixed D614/G614
Gudbjartsson et al. NEJM, 2020
Thia
Sampling May 29,2020 Sampling May 29,2020
Sampling California, June 19, 2020
California, N = 863 San Francisco, N = 147
San Diego, N = 166 Santa Clara Dept. of Public Health, N = 164
San Joaquin, N = 52 Ventura, N = 43
Date 2020 Date 2020
Feb 25 Mar 10 Mar 24 Apr 07 Apr 21 May 05 May 19 Jun 02 Feb 25 Mar 10 Mar 24 Apr 07 Apr 21 May 05 May 19 Jun 02
150
75
20
40
12
6
20
20
40
40
6
12
0
0
0
0
0
0
Weekly running counts
C
A B

Santa Clara County Iceland
Early cases of the G clade
?
?
Nearly all G614 samples are labeled “Stanford”,
suggesting a local cluster with Santa Clara county.
?
D614 persists among
Santa Clara Dept. of
Public Health samples
Population
High Risk Screening
Targeted testing: Jan 31 – Mar 15 157 sequences
Travelers to high risk places plus people with contacts
Before the black line ~50% of samples from travelers from
Austria/Italy/Switzerland, dominated by D614
Population Screening, Mar 13 - Apr 1, 85 sequences
G614 began to be first detected in this time frame
A B
CD
9692 TTTG (71.65%)
3835 CCCA (28.35%)
51 TCTG 5 CTCG
32 TTCG 4 CCTA
13 CTTG 3 TCTA
11 TCCA 2 CTTA
9 TCCG 2 CTCA
7CCCG 1 TTCA
6 TTTA 1 CCTG
11805 TTG (72.03%)
4582 CCA (27.96%)
53 CTG
39 TCG
16 CCG
9 TTA
8 CTA
5 TCA
1 ACA
Earliest examples in GISAID:
TTCG: Germany, Jan 2020: cluster of cases late Jan.-Feb.
One example: Germany/BavPat1/EPI_ISL_406862|2020-01-28
TTCG: Sampled several times in China, e.g.:
Sichuan/SC-PHCC1-022/EPI_ISL_451345|2020-01-24
Shanghai/SH0025/EPI_ISL_416334|2020-02-06
Guangzhou/GZMU0019/EPI_ISL_429080|2020-02-05
CCCG: Sampled twice in early Feb., Wuhan and Thailand
Thailand/Samut_prakarn_840/EPI_ISL_447919|2020-02-04
Wuhan/HBCDC-HB-06/EPI_ISL_412982|2020-02-07
TTTG: First identified in Italy; within 10 days sampled in
many in countries in Europe, the USA, Mexico
First sample: Italy/CDG1/2020|EPI_ISL_412973|2020-02-20
G-clade mutations (C3037T, C14408T, A23403G) CCA -> TTG
Plus the linked mutation in the UTR: C241T CCCA -> TTTG
G614is common
only in Santa Clara
samples labeled
Stanford
Santa Clara county is
particularly well sampled
in California March
Weekly running counts
Weekly running counts
Variants:
Targeted testing: Mar 15 – Mar 31
More travelers from the USA and UK, mixed D614/G614
Gudbjartsson et al. NEJM, 2020
Weekly running counts
Date 2020
A B
TTCG rare
CCCA dominant
TTCG
CCCA
CCCA
TTTG
TTTG

10

Artic Primer 81 Left
943
Raw Data
Adapter Trimmed
Adapter & Primer Trimmed
Nanopore Ligation Sequencing Adapter
Adapter/Reference Match
Primer Trimmed RawTrimmed
SHEF−BFE8E
SHEF−BFE9D
SHEF−BFEAC
SHEF−BFEBB
SHEF−BFECA
SHEF−BFED9
SHEF−BFEE8
SHEF−BFEF7
SHEF−BFF03
SHEF−BFF12
SHEF−BFF21
SHEF−BFF30
SHEF−BFF4F
SHEF−BFF5E
SHEF−BFF6D
SHEF−BFF7C
SHEF−BFF8B
SHEF−BFF9A
SHEF−BFFA9
SHEF−BFFB8
SHEF−BFFC7
SHEF−BFFD6
SHEF−BFFE5
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Sample
Percent of All Bases
Base A TG C DELREFSKIP
A
B