Evolution and molecular characteristics of SARS-CoV-2 genome · genome of SARSCoV-2 - in order to develop a comprehensive understanding of the virus and provide a basis for designing

Evolution and molecular characteristics of SARS-CoV-2 genome

Yunmeng Bai1#, Dawei Jiang1#, Jerome R Lon1#, Xiaoshi Chen1, Meiling Hu1, Shudai

Lin1, Zixi Chen1, Yuhuan Meng2*, Hongli Du1*

1. School of Biology and Biological Engineering, South China University of

Technology, Guangzhou, China

2. Guangzhou KingMed Transformative Medicine Institute Co., Ltd, Guangzhou,

China

# Contributed equally as co-first authors

*Correspondence address should be addressed to Yuhuan Meng (zb-

[email protected]) and Hongli Du ([email protected]) Abstract

In the evolution analysis of 622 complete human severe acute respiratory syndrome coronavirus 2

(SARS-CoV-2) genomes with high quality, the estimated Ka/Ks ratio of SARS-CoV-2 is 1.008,

which is significantly higher than that of SARS-CoV and MERS-CoV, and the time to the most

recent common ancestor (tMRCA) of SARS-CoV-2 is inferred in late September 2019 (95% CI:

2019/08/28-2019/10/26), which indicating that SARS-CoV-2 may have completed a positive

selection pressure of the cross-host evolution in the early stage and be going through a neutral

evolution at present. In addition, no-root phylogenetic tree of the 622 SARS-CoV-2 genomes were

constructed by maximum likelihood (ML) with the bootstrap value of 100. According to the

phylogenetic trees, all genomes were divided into Cluster 1 to 3, in which genomes were mainly

from North America, global and Europe respectively. Further we find 9 key specific sites of highly

linkage which play a decisive role in the classification of each cluster. Among them, 3 and 4 sites

of almost complete linkage are the specific sites for Cluster 1 and Cluster 3 respectively. Notably

the frequencies of haplotype TTTG and H1 are generally high in European countries and correlated

to death rate (r>0.4) based on more than 3500 SARS-CoV-2 genomes, which indicated that the

haplotypes might be related to pathogenicity of SARS-CoV-2 and need to be addressed. According

to haplotype changes in chronological order, the H3 haplotype subgroup disappeared soon after

detection, while H1 haplotype subgroup was globally increasing with time. The evolution and

molecular characteristics of more than 3500 genomic sequences provided a new perspective for

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted April 24, 2020. . https://doi.org/10.1101/2020.04.24.058933doi: bioRxiv preprint

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https://doi.org/10.1101/2020.04.24.058933

revealing the epidemiology mechanism of SARS-CoV-2 and coping with SARS-CoV-2 effectively.

1. Introduction

The global outbreak of SARS-CoV-2 is currently and increasingly recognized as a serious, public

health concern worldwide. To date, a total of seven types of coronaviruses that can infect humans

have been found, among which the more typical ones are SARS-CoV, MERS-CoV and SARS-CoV-

2. They spread across species and usually cause mild to severe respiratory diseases. The total number

of SARS-CoV and MERS-CoV infections are only 8,069 and 2,494 with reproduction number (R0)

fluctuates 2.5-3.9 and 0.3-0.8, respectively. However, SARS-CoV-2 has infected 2471136 people in

212 countries up to April 22th1, with the basic R0 ranging from 1.4 to 6.492. Among these three

typical coronavirus, MERS-CoV has the highest death rate of 34.40%, SARS-CoV has the modest

death rate of 9.59%, SARS-CoV-2 is about 6.99% and 7.69% death rate of the global and Wuhan,

but is quite high death rate in some countries of Europe, such as Belgium, Italy, United Kingdom,

Netherlands, Spain and France, which even reaches 14.95%, 13.39%, 13.48%, 11.61%, 10.42% and

13.60% respectively according to the data of April 22th, 2020. Except for the shortage of medical

supplies, aging and other factors, it is not clear whether there is virus mutation effect in these

countries with such a significantly increased death rate.

It has been reported that SARS-CoV-2 belongs to beta-coronavirus and is mainly transmitted by the

respiratory tract, which belongs to the same subgenus (SarbeCoVirus) as SARS-CoV3. Through

analyzing the genome and structure of SARS-CoV-2, its receptor-binding domain (RBD) was found

to bind with angiotensin-converting enzyme 2 (ACE2), which is also one of the receptors for binding

SARS-CoV4. Some early genomic studies have shown that SARS-CoV-2 is similar to certain bat

viruses (RaTG13, with the whole genome homology of 96.2%5) and Malayan pangolins

coronaviruses (GD/P1L and GDP2S, with the whole genome homology of 92.4%6). They have

speculated several possible origins of SARS-CoV-2 based on its spike protein characteristics

(cleavage sites or the RBD)5-7, in particular, SARS-CoV-2 exhibits 97.4% amino acid similarity to

the Guangdong pangolin coronaviruses in RBD, even though it is most closely related to bat

coronavirus RaTG13 at the whole genome level. However, it is not enough to present genome-wide

evolution by a single gene or local evolution of RBD, whether bats or pangolins play an important

role in the zoonotic origin of SARS-CoV-2 remains uncertain8. Furthermore, since there is little

molecular characteristics analysis of SARS-CoV-2 based on a large number of genomes, it is


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difficult to determine whether the virus has significant variation that affects its phenotype. Thereby,

it is necessary to further reveal the phylogenetic evolution and molecular characteristics of the whole

genome of SARS-CoV-2 in order to develop a comprehensive understanding of the virus and

provide a basis for designing vaccines and antiviral treatment strategies. Fortunately, with the

increase of SARS-CoV-2 genome data, we have an opportunity to reveal phylogenetic evolution

and molecular characteristics of SARS-CoV-2 more comprehensively. In this study, the Ka/Ks ratio,

substitution rate and the time to the most recent common ancestor (tMRCA) of SARS-CoV-2,

SARS-CoV and MERS-CoV were analyzed using KaKs_Calculator and BEAST, and then 622

SARS-CoV-2 genome sequences with high quality were clustered using maximum likelihood (ML)

with bootstrap of 100 to ensure the reliability of evolutionary trees. According to the clusters of

phylogenetic trees, the characteristic mutations of SARS-CoV-2 were screened and their potential

significance were explored. The results of this study will provide a landscape for us to further

understand SARS-CoV-2, and supply a possible basis for the prevention and treatment of SARS-

CoV-2 in different areas.

2. Materials and methods

2.1 Genome Sequences

The complete genome sequences of SARS-CoV-2 were downloaded from China National Center

for Bioinformation (https://bigd.big.ac.cn/ncov/release_genome/) and GISAID

(https://www.gisaid.org/) up to March 22th, 2020. The sequences were filtered out according to the

following criteria: (1) sequences with ambiguous time; (2) sequences with low quality which

contained the counts of unknown bases > 15 and degenerate bases > 50

(https://bigd.big.ac.cn/ncov/release_genome); (3) sequences with similarity of 100% were removed

to unique one. Finally, 624 high quality genomes with precise collection time were selected and

aligned using MAFFT v7 with automatic parameters. Besides, the genome sequences of 7 SARS-

CoV and 475 MERS-CoV were also downloaded from NCBI (https://www.ncbi.nlm.nih.gov/), and

the MERS-CoV dataset including samples collected from both human and camel.

2.2 Estimate of evolution rate and the time to the most recent common ancestor for SARS-

CoV, MERS-CoV, and SARS-CoV-2

The average Ka, Ks and Ka/Ks for all coding sequences were calculated using KaKs_Calculator

v1.29, and the substitution rate and tMRCA were estimated using BEAST v2.6.210. The temporal


https://www.ncbi.nlm.nih.gov/

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signal with root-to-tip divergence was visualized in TempEst v1.5.311 using a ML whole genome

tree as input. For SARS-CoV and SARS-CoV-2, we selected a strict molecular clock and Coalescent

Exponential Population Model. For MERS-CoV, we selected a relaxed molecular clock and Birth

Death Skyline Serial Cond Root Model. We used the tip dates and chose the HKY as the site

substitution model in all these analyses. Markov Chain Monte Carlo (MCMC) chain length was set

to 10,000,000 steps sampling after every 1000 steps. The output was examined in Tracer v1.6

(http://tree.bio.ed.ac.uk/software/tracer/).

2.3 Variants calling of SARS-CoV-2 genome sequences

Each genome sequence was aligned to the Wuhan-Hu-1 reference genome (NC_045512.2) using

bowtie2 with default parameters12, and variants were called by samtools (sort; mpileup -gf) and

bcftoots (call -vm). The merge VCF files were created by bgzip and bcftools (merge --missing-to-

ref)13,14.

2.4 Phylogenetic tree construction and virus isolates clustering for SARS-CoV-2

After alignment of 624 SARS-CoV-2 genomes with high quality and manually deleted 2 highly

divergent genomes (EPI_ISL_415710, EPI_ISL_414690) according to the firstly constructed

phylogenetic tree, the aligned dataset was phylogenetically analyzed. The SMS method was used to

select GTR+G as the base substitution model15, and the MEGA16 and PhyML 3.117 were used to

construct the phylogenetic tree by the maximum likelihood method with the bootstrap value of 100.

The online tool iTOL18 was used to visually beautify the phylogenetic tree. The clusters were

defined by the shape of phylogenetic tree.

2.5 Detection of specific sites from each Cluster

Information (ID, countries/regions and collection time) and variants (NC_045512.2 as reference

genome) of each genome from each Cluster were extracted. The allele frequency and nucleotide

divergency (pi) for each site in the virus population of each Cluster were measured by vcftools19.

The Fst were also calculated by vcftools19 to assess the diversity between the Clusters. Sites with

high level of Fst together with different major allele in each Cluster were filtered as the specific

sites. PCA were analyzed by the GCTA v1.93.1beta20 with the specific sites and all SNV dataset

respectively.

2.6 Linkage analysis of specific sites and characteristics of main haplotype subgroups

The linkage disequilibrium of the specific sites for 622 genomes and the following redownloaded


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genomes were analyzed by haploview21, and the statistics of the haplotype of the specific sites for

each Cluster or country were used in-house perl script.

2.7 Frequencies of specific sites or haplotypes and correlation with death rate

We redownloaded the sequences from GISAID up to April 6th, 2020 to further explore possible

roles of the specific sites, and obtained 3624 genome sequences from the countries with more than

10 complete genome sequences. The frequencies of specific sites for each country were

calculated. The death rate was estimated with Total Deaths /Confirmed Cases based on the data

from Johns Hopkins resources on April 22th, 2020

(https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6).

The correlation coefficient between death rate and frequencies of specific site or haplotype in

different countries was calculated using Pearson method.

3. Results

3.1 Genome Sequences

We got a total of 1053 sequences up to March 22th, 2020. According to the filter criteria, 37

sequences with ambiguous time, 314 with low quality and 78 with similarity of 100% were removed.

A total of 624 sequences were obtained to perform multiple sequences alignment. Two highly

divergent sequences (EPI_ ISL_414690, EPI_ISL_415710) according to the firstly constructed

phylogenetic tree were also filtered out (Extended Data Table 1). The remaining 622 sequences

were used to reconstruct a phylogenetic tree.

3.2 Estimate of evolution rate and the time to the most recent common ancestor for SARS-

CoV, MERS-CoV, and SARS-CoV-2

In this study, date for SARS-CoV-2 ranged from 2019/12/26 to 2020/03/18 was collected. The

average Ka/Ks of all the coding sequences was closer to 1 (1.008), which indicating the genome

was going through a neutral evolution. We also reevaluated the Ka/Ks of SARS-CoV and MERS-

CoV through the whole period, and found the ratio were significantly smaller than SARS-CoV-2

(Table 1). To estimate more credible Ka/Ks for SARS-CoV-2, we recalculated it using

redownloaded 3624 genome sequences ranged from 2019/12/26 to 2020/04/06, and the average

Ka/Ks of it was 1.009, which was almost same with the above result.

We assessed the temporal signal using TempEst v1.5.3. All three data sets exhibit a positive


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correlation between root-to-tip divergence and sample collecting time (Extended Data Fig 1),

hence they are suitable for molecular clock analysis in BEAST11. The substitution rate of SARS-

CoV-2 genome was estimated to be 1.60 x 10-3 (95% CI: 1.42 x 10-3 - 1.80 x 10-3, Table 2, Extended

Data Fig 2a) substitution/site/year, which is as in the same order of magnitude as SARS-CoV and

MERS-CoV. The tMRCA was inferred on the late September, 2019 (95% CI: 2019/08/28-

2019/10/26,Table 2, Extended Data Fig 2b), about 2 months before the early cases of SARS-CoV-

222.

3.3 Phylogenetic tree and clusters of SARS-CoV-2

We firstly constructed the phylogenetic tree using PhyML 3.1 with fast likelihood-based method

(aLRT SH-like), while it failed when set the bootstrap value as 100 due to no response for a long

time, so that MEGA was used with GTR+G model and the bootstrap value was set to 100. According

to the shape of phylogenetic trees (Fig 1, Extended Data Fig 3a and b), we divided 622 sequences

into three clusters: Cluster 1 including 76 sequences mainly from North America, Cluster 2

including 367 sequences from all regions of the world, and Cluster 3 including 179 sequences

mainly from Europe (Extended Data Table 2).

3.4 The specific sites of each Cluster

The Fst and population frequency of a total of 9 sites (NC_045512.2 as reference genome) were

detected (Table 3, Extended Data Table 3). Thereinto, three (C17747T, A17858G and C18060T)

are the specific sites of Cluster 1, and four (C241T, C3037T, C14408T and A23403G) are the

specific sites of Cluster 3. Notably, C241T was located in the 5’-UTR region and the others were

located in coding regions (6 in ofr1ab gene, 1 in S gene and 1 in ORF8 gene). Five of them were

missense variant, including C14408T, C17747T and A17858G in ofr1ab gene, A23403G in S

gene, and C28144T in ORF8 gene. The PCA results showed that these 9 specific sites could

clearly separate the three Clusters, while all SNV dataset could not clearly separate Cluster 1

and Cluster 2 (Extended Data Fig 4), which further suggested that these 9 specific sites are

the key sites for separating the three Clusters.

3.5 Linkage of specific sites and characteristics of main haplotype subgroups

We found the 9 specific sites are highly linkage based on 622 genome sequences (Fig 2a), then we

carried out a further linkage analysis using the redownloaded 3624 genome sequences (Fig 2b). As

a result, for the 3624 genome sequences, 3 specific sites in the Cluster 1 were almost complete


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linkage, and haplotype CAC and TGT accounted for 98.65% of all the 3 site haplotypes. The same

phenomenon was also found in 4 specific sites of Cluster 3, and haplotype CCCA and TTTG

accounted for 97.68% of all the 4 site haplotypes. Intriguingly, the 9 specific sites were still highly

linkage, and four haplotypes, including TTCTCACGT (H1), CCCCCACAT (H2), CCTCTGTAC

(H3) and CCTCCACAC (H4), accounted for 95.89% of all the 9 site haplotypes. The frequencies

of each site and main haplotype for each country were showed in Fig 3 and Extended Data Table

4. The data of 3624 genomes showed that the haplotype TTTG of the 4 specific sites in Cluster 3

had existed globally at present, and still exhibited high frequencies in most European countries but

quite low in Asian countries. While the haplotype TGT of the 3 specific sites in Cluster 1 existed

almost only in North America and Australia. For the 9 specific sites, most countries had only two or

three main haplotypes, except America, Australia and United Kingdom had all of four main

haplotypes.

All haplotypes of 9 specific sites and the numbers of them were shown in Extended Data Table 5,

and the numbers of these haplotypes detected in each country in chronological order were shown in

Fig 4a. From these results, H2 and H4 haplotype subgroups had existed for a long time (2019-12-

31 to 2020-04-14), and the H3 haplotype subgroup disappeared soon after detection (2020-02-18 to

2020-04-07), while H1 haplotype subgroup was globally increasing with time, which indicated H1

subgroup had adapted to the human hosts, and was under an adaptive growth period worldwide.

However, due to the nonrandom sampling on early phase (only patients having a recent travel to

Wuhan were detected), some earlier cases of H3 may be lost, the high proportion of H3 subgroup

during Feb 18 to 25 could confirm this point.

From the whole genome mutations in each haplotype subgroup (Fig 4b, Extended Data Table 6),

we found that except the 9 specific sites, there was no common mutations with frequencies more

than 0.01 in these haplotype subgroups but between H2 and H4 subgroups. H3 and H4 subgroups

had the lowest Ka/Ks ratio (0.795 and 0.796 respectively) among the 4 main subgroups (Table 4),

suggesting that H3 and H4 subgroup might be going through a purifying evolution and have existed

for a long time, while H2 (1.268) and H1 subgroup (1.099) might be going through a positive

selection and a neutral evolution according to their Ka/Ks ratios, respectively, which were consistent

with the above phenomenon that only H1 subgroup was expanding with time around the world.

3.6 Correlation analysis of specific sites with death rate


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To explore the relationship between death rate and the 9 specific sites, we used Pearson method to

calculate the correlation coefficient between death rate and frequency of each specific site or main

haplotype in 17 countries. As a result, all r values of 241T, 3037T, 14408T, 23403G and haplotype

TTTG and H1 in Cluster 3 were more than 0.4 (Fig 5, Extended Data Table 4). This finding

integrating their high frequencies in most European countries indicated that the 4 sites and haplotype

TTTG and H1 might be related to the pathogenicity of SARS-CoV-2.

4. Discussion

SARS-CoV-2 poses a great threat to the production, living and even survival of human beings23. With

the further outbreak of SARS-CoV-2 in the world, further understanding on the evolution and molecular

characteristics of SARS-CoV-2 based on a large number of genome sequences will help us cope better

with the challenges brought by SARS-CoV-2. In the present study, we estimated the Ka/Ks ratio and

tMRCA of SARS-CoV-2, SARS-CoV and MERS-CoV, and constructed the phylogenetic tree of SARS-

CoV-2 genomes. As a result, we found that 9 specific sites of highly linkage could play a decisive role in

the classification of SARS-CoV-2, and the haplotype TTTG might be related to pathogenicity of SARS-

CoV-2 based on more than 3500 genomes. All these results could accelerate our further understanding of

SARS-CoV-2.

Exploring evolution rate, tMRCA and phylogenetic tree of SARS-CoV-2 would help us better

understand the virus24. The average Ka/Ks for all the coding sequences of 622 and 3624 SARS-

CoV-2 genomes was 1.008 and 1.009, which was significantly higher than those of SARS-CoV and

MERS-CoV, indicating that the SARS-CoV-2 was going through a neutral evolution. In general,

SARS-CoV would show purifying selection pressures and the Ka/Ks would go down at the middle

or end of the epidemic25. Therefore, it seemed to be reasonable considering that SARS-CoV-2 was

under an exponential growth period around the world. Interestingly, we also found that the

subgroups of different haplotypes (H1, H2, H3 and H4) seemed to experience different evolutionary

patterns according to their Ka/Ks. The H3 haplotype subgroup disappeared soon after detection

(2020-02-18 to 2020-04-07) (2020-02-18 to 2020-04-07, Fig 4a), while H1 haplotype subgroup was

globally increasing with time, these evolution and changes should be considered in developing

therapeutic drugs and vaccines as soon as possible. The tMRCA of SARS-CoV-2 was inferred on

the late September, 2019 (95% CI: 2019/08/28- 2019/10/26), about 2 months before the early cases


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of SARS-CoV-222. We also estimated tMRCA of SARS-CoV and MERS-CoV with the same

methods, both were about 3 months later than the corresponding tMRCAs estimated by previous

studied26,27, which indicated the tMRCA of SARS-CoV-2 we estimated was reasonable.

A recent study clustered 160 SARS-CoV-2 whole-genome sequences into A, B and C groups by a

phylogenetic network analysis28. The cluster result was similar to our study: both the samples in

Cluster A and our Cluster 1 were mainly from the United States; the samples in Cluster C and our

Cluster 3 were mainly from European countries, while Cluster B and our Cluster 2 were mainly

from China and the other regions. It was interesting that the markers in Cluster B (T8782C and

C28144T) were also found in our study, but the other markers in Cluster A (T29095C) and Cluster

C (G26144T) were not significantly in our study. That may be caused by different sample sizes and

different constructing methods of phylogenetic tree. Based on the base substitution model, the ML

method avoids the possible "long-branch attraction" problem in the maximum parsimony method

and is faster than the Bayesian method29, hence it could be used as a reliable method for

phylogenetic analysis. Nowadays, there is still a shortage of studies on phylogenetic analysis of

SARS-CoV-2. Some studies used the genome of bat SARS-like-CoV30, RaTG1330 or MT019529

from Wuhan (https://bigd.big.ac.cn/ncov/tree) as the root of phylogenetic tree. Unfortunately, there

was no obvious evidence showing that SARS-CoV-2 was from the bat coronavirus even the identity

between SARS-CoV-2 and RaTG13 was up to 96.2%5. In our study, the tMRCA of SARS-CoV-2

was inferred on the late September 2019, which indicated there might exist an earlier SARS-CoV-2

strain we didn't find. Then in the case of unclear source of SARS-CoV-2 and high homology of its

genomes (> 99.9% homology), it may be inappropriate to identify the evolutionary characteristics

inside the genomes by taking bat SARS-like-CoV, RaTG13 or MT019529 as root. Therefore, the

maximum likelihood method was used in current study to construct a no-root tree to obtain the

reliable clusters with different characteristics. Based on the no-root tree, we identified 9 specific

sites of highly linkage that play a decisive role in the classification of clusters successfully. Among

the four main haplotypes, H1 and H3 were in Cluster 3 and Cluster 1, respectively, while there were

two haplotypes H2 and H4 in Cluster 2 (Fig 1, Extended Data Fig 3b).

Among the 9 specific sites, 8 of them were located in coding regions (6 in ofr1ab gene, 1 in S gene and

1 in ORF8 gene), and 5 of them were missense variant, including C14408T, C17747T and A17858G in

ofr1ab gene, A23403G in S gene, and C28144T in ORF8 gene. Orf1ab is composed of two partially


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overlapping open reading frames (orf), orf1a and 1b. It is proteolytic cleaved into 16 non-structural

proteins (nsp), including nsp1 (suppress antiviral host response), nsp9 (RNA/DNA binding activity),

nsp12 (RNA-dependent RNA polymerase), nsp13 (helicase) and others31, indicating the vital role

of it in transcription, replication, innate immune response and virulence32. C14408T with high

frequencies of T in European countries were located at nsp12 region, indicating that this missense

variant might influence the role of RNA polymerase. Spike glycoprotein, the largest structural

protein on the surface of coronaviruses, comprises of S1 and S2 subunits which mediating binding

the receptor on the host cell surface and fusing membranes, respectively33. It has been reported that

S protein of SARS-CoV-2 can bind angiotensin-converting enzyme 2 (ACE2) with higher affinity

than that of SARS4,5. Whether the missense variant of A23403G in S gene, which with high

frequencies of G in European countries, will change the affinity between S protein and ACE2 remains

to be further investigated.

Previous studies showed that the death rate of SARS-CoV-2 is affected by many factors, such as

medical supplies, aging, life style and other factors34,35. However, our study found that the 4 specific

sites (C241T, C3037T, C14408T and A23403G) in the Cluster 3 were almost complete linkage, and the

frequency of haplotype TTTG was generally high in European countries and correlated to death rate

(r>0.4), which provides a new perspective to the reasons of relatively high death rate in Europe. In

order to make frequency relatively credible, we selected countries with more than 10 genomes

(except South Korea with 16 genomes, all the other countries with more than 20 genomes) to analyze

the correlation between frequencies of haplotypes and death rate. Therefore, the correlation between

the frequencies of haplotypes and death rate obtained in this study is quite reliable, and these specific

sites should be considered in designing new vaccine and drug development of SARS-CoV-2.

5. Conclusion and prospective

The Ka/Ks ratio of SARS-CoV-2 (1.008) and tMRCA of SARS-CoV-2 (95% CI: 2019/08/28-

2019/10/26) indicated that SARS-CoV-2 might have completed the positive selection pressure of

cross-host evolution in the early stage and be going through a neutral evolution at present. The 9

specific sites with highly linkage were found to play a decisive role in the classification of clusters.

Thereinto, 3 of them are the specific sites of Cluster 1 mainly from North America, 4 of them are

the specific sites of Cluster 3 mainly from Europe, both of these 3 and 4 specific sites are almost


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complete linkage. The frequencies of haplotype TTTG for the 4 specific sites in Cluster 3 were

generally high in European countries and correlated to death rate (r>0.4) based on more than 3500

SARS-CoV-2 genomes, which suggested this haplotype might relate to pathogenicity of SARS-

CoV-2 and provided a new perspective to the reasons of relatively high death rates in Europe. The

relationship between the haplotype TTTG and the pathogenicity of SARS-CoV-2 needs to be further

verified by clinical samples or virus virulence test experiment, and the 9 specific sites with highly

linkage may provide a starting point for the traceability research of SARS-CoV-2. Given that the

different evolution patterns of different haplotypes subgroups, we should consider these evolution

and changes in the development of therapeutic drugs and vaccines as soon as possible.

Acknowledgements

This work was supported by the National Key R&D Program of China (2018YFC0910201), the

Key R&D Program of Guangdong Province (2019B020226001), the Science and the Technology

Planning Project of Guangzhou (201704020176) and the Science and Technology Program of

Guangzhou (2020Q-P013).

Author contributions

H.D. conceived the study. Y.M., Y.B., D.J., J.L and X.C. carried out the data analysis and wrote the

manuscript. M.H., S.L., and Z.C. collected data and revised the manuscript. H.D. and Y.M.

supervised the whole work, attended the discussions and revised the manuscript.

Competing interests

The authors declare no competing interests.

Data availability Sequence data that support the findings of this study could be downloaded in China National Center for Bioinformation(https://bigd.big.ac.cn/ncov/release_genome/), GISAID (https://www.gisaid.org/) and NCBI (https://www.ncbi.nlm.nih.gov/). Code availability The Perl script is available on Github (https://github.com/yunmengbai/SARS-CoV-2-genome.git).


https://bigd.big.ac.cn/ncov/release_genome/

https://www.ncbi.nlm.nih.gov/

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Figure Legends

Figure 1. Phylogenetic tree and clusters of 622 SARS-CoV-2 genomes. The 622 sequences

were clustered into three clusters: Cluster 1 were mainly from North America, Cluster 2 were

from regions all over the world, and Cluster 3 were mainly from Europe.

Figure 2. Linkage disequilibrium plot of haplotypes of the 9 specific sites. a, The plot for 622


https://doi.org/10.1101/2020.04.24.058933

genome sequences; b, The plot for 3624 genome sequences.

Figure 3. The frequencies of both the 9 specific sites and haplotypes. The frequencies of the 9

specific sites (a) and haplotypes (b) in each country which with more than 10 genome sequences

up to April 6th, 2020.

Figure 4. The characteristics of haplotype subgroups. a, The numbers of haplotypes of the 9

specific sites detected in each country in chronological order (In order to get the latest trend of

haplotype subgroup in chronological order, we had updated the genome data to April 20th,

2020); b, The whole genome mutations in each main haplotype subgroup.

Figure 5. The correlation between death rate and frequencies of both the 9 specific sites and

haplotypes.

Tables

Table 1. Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of the SARS-CoV, MERS-

CoV and SARS-CoV-2 genome sequences

Table 2. Substitution rate and tMRCA estimated by BEAST v2.6.2

Table 3. The information of the 9 specific sites in each Cluster.

Table 4. Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of each 4 main haplotype

genomes

Extended Data

Extended Data Figure 1. Regression analyses of the root-to-tip divergence with the maximum-

likelihood phylogenetic tree of SARS-CoV (a), MERS-CoV (b) and SARS-CoV-2 (c). The

root-to-tip genetic distances were inferred in TempEst v1.5.3. Three data sets exhibit a positive

correlation between root-to-tip divergence and sample collecting time, and they appear to be

suitable for molecular clock analysis.

Extended Data Figure 2. Substitution rate and tMRCA estimate of SARS-CoV-2

a, Estimated substitution rate of SARS-CoV-2 using BEAST v2.6.2 under a strict molecular

clock. The mean rate was 1.60 x 10-3 (substitutions per site per year). b, The estimated tMRCA

of SARS-CoV-2 using BEAST v2.6.2 under a strict molecular clock. The mean tMRACA was

2019.74 (date: 2019-09-27).


https://doi.org/10.1101/2020.04.24.058933

Extended Data Figure 3. Phylogenetic tree of 622 SARS-CoV-2 genomes. a, Phylogenetic tree

constructed by MEGA with bootstrap value of 100. b, Phylogenetic tree constructed by PhyML

3.1.

Extended Data Figure 4. The PCAs analysis result based on the 9 specific sites (a) and all SNVs

(b).

Extended Data Table 1. The information of the first downloaded complete genome sequences.

Extended Data Table 2. The sequence IDs in each Cluster.

Extended Data Table 3. The details information of the SNVs in all Clusters.

Extended Data Table 4. The information of death rate, frequencies of the 9 specific sites and

main haplotypes in each country for 3624 genomes.

Extended Data Table 5. All haplotypes of the 9 specific sites and numbers of them for 3624

genomes.

Extended Data Table 6. Whole genome mutations and frequencies in each main haplotype

subgroup (NC_045512.2 as reference genome)


https://doi.org/10.1101/2020.04.24.058933

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MT012098_2020/01/27_India/KeralaState

MT184912_2020/02/17_UnitedStates

EPI_ISL_415654_2020/03/09_Europe/France/HautsdeFrance/Compigne

EPI_ISL_415657_2020/03/12_Europe/UnitedKingdom/Wales

EPI_ISL_416382_2020/02/08_Asia/China/Shanghai

dn

alkc

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ala

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N/ai

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2/2

0/0

20

2_

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43

14

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SI_I

PE

MT019530_2019/12/30_China/Hubei/Wuhan

EPI_IS

L_4160

31_202

0/03/0

4_Sout

hAmeri

ca/Bra

zil/Sa

oPaulo

/SaoPa

ulo

EPI_ISL_415458_2020/

03/01_Europe/Switzer

land

EPI_ISL_415706_2020/03/06_Europe/Switzerland

EPI_IS

L_4149

41_202

0/01/3

0_Asia

/China

/Shand

ong

EPI_ISL_

414577_2

020/03/0

2_SouthA

merica/C

hile/Tal

ca

EPI_ISL_416035_2020/03/05_SouthA

merica/Brazil/SaoPaulo/SaoPaulo

EPI_ISL_415534_2020/03/11_Europe/Netherlands/ZuidHolland


EPI_ISL_415158_2020/03/01_Europe/Belgium/Brussels

EPI_ISL_416033_2020/03/03_SouthAmerica/Brazil/SaoPaulo/SaoPauloEPI_ISL_414505_2020/02/27_Europe/Germany/NorthRhineWestphalia/HeinsbergDistrict

EPI_ISL_413459_2020/02/20_Asia/Japan/Tokyo

ye

ndy

S/W

SN/

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tsu

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na

ecO

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0/3

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20

2_

79

53

14

_L

SI_I

PE

EPI_ISL_415650_2020/03/02_Europe/France/IDF

MT123292

_2020/01

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a/Guangd

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gzhou

EPI_ISL_412980_2020/01/18_Asia/China/Hubei/Wuhan

EPI_ISL_414452_2020/03/06_Europe/Netherlands/NoordBrabant

ye

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S/W

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EPI_ISL_407896_2020/01/30_Oceania/Australia/Queensland/GoldCoast

ye

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E

EPI_ISL_408485_2020/01/18_Asia/China/Beijing

EPI_ISL_415621_2020/03/09_NorthAmerica/USA/Washington/Seattle

EPI_ISL_415105_2020/03/04_So

uthAmerica/Brazil/Bahia/Feir

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EPI_ISL_416044_2020/01/25_Asia/China/Hangzhou

ia

hg

na

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EPI_ISL_414499_2020/02/26_Europe/Germany/NorthRhineWestphalia/HeinsbergDistrict


MT163717_202

0/02/28_Unit

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hington


EPI_ISL_406593_2020/01/13_Asia/China/Guandong/Shenzhen

EPI_IS

L_4134

89_202

0/03/0

3_Euro

pe/Ita

ly/Lom

bardy/

Milan

MN997409_2020/01/22_UnitedStates/Arizona/Phoenix

EPI_ISL_416426_2020/03/17_Europe/Hungary/Budapest

EPI_ISL_406533_2020/01/22_Asia/China/Guangdong/Guangzhou

EPI_ISL_413573_2020/03/02_Europe/Netherlands/HardinxveldGiessendam

EPI_ISL_415698

_2020/02/27_Eu

rope/Switzerla

nd

EPI_ISL_

415153_2

020/03/0

3_Europe

/Belgium

/Huldenb

erg

EPI_ISL_415462_2020/03/09_Europe/Netherlands/Gelderland


MN994467_2020/01/23_UnitedStates/California/LosAngeles

EPI_ISL_415157_2020/03/01_Europe/Belgium/Sint-Niklaas

EPI_ISL_413582_2020/03/01_Europe/Netherlands/Rotterdam

EPI_ISL_413996_2020/02/24_Europe/Switzerland/Tessin

EPI_ISL_

416469_2

020/03/0

3_Europe

/Belgium

/Kessel-

Lo

EPI_IS

L_4136

02_202

0/03/0

3_Euro

pe/Fin

land/H

elsink

i

EPI_ISL_416467_2020/03/02_Europe/Belgium/Holsbeek

EPI_ISL_414621_2020/03/08_NorthAmerica/USA/Washington/Kirkland

EPI_ISL_411066_2020/01/22_Asia/China/Fujian

MN908947_2019/12/30_China/Hubei/Wuhan

dleiff

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S/eri

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0/3

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20

2_

00

54

14

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SI_I

PE

EPI_ISL_416507_2020/03/05_Europe/France/Bretagne/Rennes

MT007544_2020/01/25_Australia/Victoria/Clayton


EPI_ISL_414528_2020/02/10_Asia/HongKong

EPI_ISL_415641_2020/02/27_Asia/Georgia/Tbilisi

EPI_ISL_416028_2020/03/03_SouthAmerica/Brazil/SaoPaulo/SaoPaulo


EPI_ISL_416475_2020/03/03_Europe/Belgium/Leuven


EPI_ISL_415456_2020/02/29_Europe/SwitzerlandEPI_ISL_414022_2020/02/27_Europe/Switzerland/Geneva

EPI_ISL_415541_202

0/03/13_NorthAmeri

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AS

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2/2

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20

2_

67

44

14

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SI_I

PE


EPI_ISL_414593_2

020/03/05_NorthA

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EPI_ISL_416466_2020/03/03_NorthAmerica/USA/Washington/KingCounty

MT066176_2020/02/05_China/Taiwan/Taipei

EPI_ISL_41

4023_2020/

03/01_Euro

pe/Switzer

land/Vaud



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au

H/us

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84

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4_

LSI

_IP

E

EPI_ISL_410714_2020/02/03_Asia/Singapore


EPI_ISL_414007_2020/02/28_Europe/UnitedKingdom/England


EPI_IS

L_4156

29_202

0/03/0

4_Euro

pe/Uni

tedKin

gdom/S

cotlan

d





EPI_ISL_413572_2020/03/01_Europe/Netherlands/Haarlem

EPI_ISL_415606_2020/03/08_No

rthAmerica/USA/Washington

EPI_ISL_413024_2020/02/29_Europe/Switzerland/Zurich

MT027064_2020/01/29_UnitedStates/California

EPI_ISL_415543_2020/03/13_NorthAmerica/USA/Utah

EPI_ISL_

416428_2

020/03/0

7_Asia/V

ietnam/Q

uangning


LC528232_2020/02/10_Japan/Kanagawa

EPI_ISL_414624_2020/02/26_Europe/France/Normandie/Rouen


EPI_ISL_415583_2020/03/03_NorthAmerica/Canada/BritishColumbia

EPI_ISL_415604_2020/03/10_NorthAmerica/USA/WashingtonEPI_ISL_416140_202

0/03/02_Europe/Den

mark/Copenhagen

EPI_IS

L_4149

40_202

0/01/2

5_Asia

/China

/Shand

ong



EPI_IS

L_4163

30_202

0/02/0

2_Asia

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hai

EPI_ISL_416432_2020/03/07_Asia/SaudiArabia/Riyadh

ia

hg

na

hS/

ani

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ais

A_

40/

20/

02

02

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43

61

4_

LSI

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E

EPI_ISL_415616_2020/03/08_NorthAmerica/USA/Was

hington


EPI_ISL_414554_2020/03/08_Europe/Netherlands/Utrecht

EPI_IS

L_4156

40_202

0/03/0

8_Euro

pe/Uni

tedKin

gdom/S

cotlan

d



EPI_ISL_412870_2020/01/30_Asia/SouthKorea/Seoul

EPI_IS

L_4129

79_202

0/01/1

8_Asia

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/Hubei

/Wuhan

MN975262_2020/01/11_China/Guangdong/Shenzhen

EPI_ISL_408479_2020/01/23_Asia/China/Chongqing/Zhongxian


ila

wa

H/ti

aw

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ais

A_

20/

30/

02

02

_8

54

61

4_

LSI

_IP

E



EPI_ISL_416471_2020/03/02_Europe/Belgium/Kessel-Lo

EPI_ISL_414021_2020/02/27_Europe/Switzerland/Basel

EPI_ISL_404228_2020/01/17_Asia/China/Zhejiang



EPI_ISL_414600_2020/02/26_Europe/France/Grand-Est/Strasbourg



EPI_ISL_413015_2020/01/23_NorthAmerica/Canada/Ontario


EPI_ISL_406862_2020/01/28_Europe/Germany/Bavaria/Munich

EPI_ISL_416034_2020/03/04_SouthAmerica/Brazil/SaoPaulo/SaoPa

ulo

EPI_ISL_404227_2020/01/16_Asia/China/Zhejiang

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02

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EPI_ISL_413566_2020/03/02_Europe/Netherlands/Blaricum

EPI_IS

L_4138

54_202

0/01/3

0_Asia

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/Guang

dong




EPI_IS

L_4146

31_202

0/03/0

4_Euro

pe/Fra

nce/Gr

and-Es

t/Reim

s




EPI_ISL_415454_2

020/02/28_Europe

/Switzerland



EPI_ISL_414623_2020/02/25_Europe/France/Grand-Est/Strasbourg








EPI_ISL_413647_2020/03/01_Europe/Portugal




CNA0007334_2020/01/01_China/Hubei/Wuhan


EPI_ISL_410720_2020/01/23_Europe/France/Ile-de-France/Paris

MT106054_2020/02/11_UnitedStates/Texas

EPI_IS

L_4164

94_202

0/03/0

4_Euro

pe/Fra

nce/No

rmandi

e/Roue

n




MT121215_2020/02/02_China/Shanghai


EPI_ISL_415461_2020/03/10_Europe/Netherlands/Gelderland


EPI_ISL_408484_2020/01/15_Asia/China/Sichuan/Chengdu

EPI_ISL_41

6473_2020/

01/26_Asia

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gzhou

MT188340_2020/03/07_UnitedStates/MN

EPI_ISL_

416333_2

020/01/3

0_Asia/C

hina/Sha

nghai

EPI_ISL_413853_2020/01/30_Asia/China/Guangdong


EPI_ISL_406031_2020/01/23_Asia/Taiwan/Kaohsiung

MT163719_2020/03/01_UnitedStates/Washington

EPI_ISL_416498_2020/03/11_Europe/France/IledeFrance/Garches


EPI_ISL_415652_2020/03/05_Europe/France/Bourgogne-France-Comt/Thise



MT123290_2020/02/05_China/Guangdong/Guangzhou








ye

ndy

S/W

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20

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59

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14

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SI_I

PE

EPI_ISL_403962_2020/01/08_Asia/Thailand/Nonthaburi

EPI_ISL_416032_2020/03/04_SouthAmerica/Brazil/DistritoFederal/Brasilia


EPI_ISL_413599_2020/03/04_Oceania/Australia/NSW/Sydney


EPI_ISL_416491_2020/03/15_NorthAmerica/USA/WI/Danecounty

set

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20

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119

48

1T

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EPI_ISL_416521_2020/03/10_Asia/SaudiArabia



EPI_ISL_408668_2020/01/24_Asia/Vietnam/ThanhHoa

EPI_ISL_413928_2020/03/05_NorthAmerica/USA/California/SanFrancisco


EPI_ISL_415658_2020/03/06_SouthAmerica/Chile/Santiago


EPI_ISL_413653

_2020/03/05_No

rthAmerica/USA

/Washington


EPI_ISL_414632_2020/03/04_Europe/France/Grand-Est/Reims



MN988713_2020/01/21_UnitedStates/Illinois/Chicago



EPI_ISL_416457_2020/03/18_NorthAmerica/USA/California/SanDiegoCounty


EPI_ISL_412912_2020/02/25_Europe/Germa

ny/Baden-Wuerttemberg



EPI_ISL_414521_2020/03/02_Europe/Germany/Munich


ye

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Sw

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EPI_ISL_

414559_2

020/03/0

7_Europe

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EPI_ISL_416489_2020/03/15_NorthAmerican/USA/WI/Danecounty



EPI_ISL_416514_2020/03/15_Oceania/Australia/Victoria/Melbourne

EPI_ISL_413997_2020/02/26_Europe/Switzerland/Geneva


ara

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MN938384_2020/01/10_China/Guangdong/Shenzhen




EPI_IS

L_4145

87_202

0/03/0

3_Euro

pe/Ire

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EPI_ISL_412973_2020/02/20_Europe/Italy/Lombardy

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na

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20

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36

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EPI_ISL_413513_2020/02/27_Asia/SouthKorea

EPI_ISL_409067_2020/01/29_NorthAmerica/USA/Massachusetts

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EPI_IS

L_4146

91_202

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5_Asia

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EPI_ISL_407313_2020/01/19_Asia/China/Zhejiang/Hangzhou





MT135041_2020/01/26_China/Beijing




MT072688_2020/01/13_Nepal/KathmanduEPI_ISL_415627_2020/03/09_NorthAmerica/USA/Washington

EPI_ISL_416472_2020/03/02_Europe/Belgium/Kessel-LoEPI_ISL_415648_2020/03/03_Europe/Italy

NMDC60013002-06_2019/12/30_China/Hubei/Wuhan

EPI_ISL_415156_2020/03/01_Europe/Belgium/Huldenberg

EPI_IS

L_4138

60_202

0/01/3

0_Asia

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/Guang

dong



EPI_ISL_406536_2020/01/22_Asia/China/Guangdong/Foshan

MT0504

93_202

0/01/3

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e


EPI_ISL_414601_2020/02/27_Europe/France/Normandie/Rouen



EPI_ISL_

416326_2

020/01/3

0_Asia/C

hina/Sha

nghai





EPI_IS

L_4145

25_202

0/03/0

2_Euro

pe/Uni

tedKin

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EPI_ISL_415128_2020/02/29_SouthAmerica/Brazil/EspiritoSanto/VilaVelha




EPI_IS

L_4164

70_202

0/03/0

2_Euro

pe/Bel

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outhui

n

EPI_ISL_41

6519_2020/

03/02_Ocea

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EPI_ISL_413559_2020/02/27_NorthAmerica/USA/California/SolanoCounty

ye

ndy

S/sel

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N/ail

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23

14

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SI_I

PE

LC522974_2020/01/31_Japan/Tokyo

EPI_ISL_413021_2020/02/29_Europe/Switzerland/Z

urich





EPI_ISL_414620_202

0/03/08_NorthAmeri

ca/USA

EPI_ISL_

412978_2

020/01/1

7_Asia/C

hina/Hub

ei/Wuhan




EPI_ISL_413023_2020/02

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/Zurich

EPI_ISL_

413022_2

020/02/2

9_Europe

/Switzer

land/Zur

ichEPI_ISL_403963_2020/01/13_Asia/Thailand/Nonthaburi


EPI_ISL_415591_2020/

03/10_NorthAmerica/U

SA/Washington

EPI_ISL_415630_2020/03/09_Europe/UnitedKingdom/Scotland

EPI_ISL_413019_2020/02/26_Europe/Switzerland/Zurich


EPI_ISL_416499_2020/03/11_Europe/France/IledeFrance/Longjumeau


EPI_ISL_413592_2020/03/02_Asia/Taiwan/Taipei

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N/e

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20

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77

53

14

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SI_I

PE


EPI_ISL_

413579_2

020/03/0

3_Europe

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hg

na

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ani

hC/

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A_

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02

02

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04

61

4_

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E



EPI_IS

L_4151

54_202

0/03/0

1_Euro

pe/Bel

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raaine

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na

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G/e

por

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0/3

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20

2_

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54

14

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SI_I

PE


EPI_ISL_4144

70_2020/03/0

6_Europe/Net

herlands/Zui

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EPI_ISL_

415703_2

020/03/0

1_Europe

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land

EPI_IS

L_4156

46_202

0/03/0

3_Euro

pe/Ita

ly



EPI_IS

L_4156

42_202

0/03/1

0_Asia

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lisi





EPI_IS

L_4154

35_202

0/03/0

6_Euro

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tedKin

gdom/W

ales


EPI_ISL_413014_2020/01/25_NorthAmerica/Canada/Ontario






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MT152824_2020/02/24_UnitedStates/Washington/SnohomishCounty



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46

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SI_I

PE



EPI_ISL_415594_2020/03/10_NorthAmerica/USA/WashingtonMT123291_2020/01/29_China/Guangdong/Guangzhou




LC522975_2020/01/31_Japan/Tokyo



EPI_ISL_416029_2020/03/04_

SouthAmerica/Brazil/SaoPau

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T/ai

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2/2

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20

2_

34

75

14

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SI_I

PE


EPI_ISL_41

1060_2020/

01/21_Asia

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ian

EPI_ISL_411902_2020/01/27_Asia/Cambodia/Sihanoukville



EPI_IS

L_4163

23_202

0/01/2

9_Asia

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/Shang

hai

MT066156_2020/01/30_Italy

EPI_ISL_415159_2020/02/29_Europe/Belgium/Leuven

MT0499

51_202

0/01/1

7_Chin

a/Yunn

an

EPI_ISL_414633_2020/03/04_Europe/France/Ile-de-France/Pontoise






EPI_ISL_415614_2020/03/09_NorthA

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EPI_ISL_415649_2020/03/05_Europe/France/Hauts-de-France/Crpy-en-Valois


EPI_ISL_412459_2020/01/08_Asia/China/Hubei/Jingzhou


MT163718_202

0/02/29_Unit

edStates/Was

hington




EPI_ISL_4154

59_2020/02/2

9_Europe/Swi

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ve

EPI_ISL_412873_2020/02/06_Asia/SouthKorea/Chungcheongnam-do

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82

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PE

EPI_ISL_415460_2020/03/09_Europe/Netherlands/Flevoland


EPI_ISL_410546_2020/01/31_Europe/Italy/Rome

EPI_ISL_

414689_2

020/02/2

5_Asia/C

hina/Gua

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N/ail

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57

92

14

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SI_I

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EPI_ISL_411952_2020/01/24_Asia/China/Jiangsu




EPI_ISL_41

5584_2020/

03/02_Nort

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anada/Brit

ishColumbi

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EPI_ISL_41

6431_2020/

03/06_Asia

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anoi

EPI_IS

L_4079

76_202

0/02/0

3_Euro

pe/Bel

gium/L

euven

EPI_IS

L_4119

26_202

0/01/2

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Tree scale: 0.0001

Inner Color: Continents

Asia

Europe

NorthAmerica

Oceania

SouthAmerica

Outer Clolors: Countries

Australia

Belgium

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Cambodia

Canada

Chile

China

Denmark

Finland

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Shape Plot: Haplotype

H1

H2

H3

H4

H5

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Other

Cluster 1

Cluster 2

Cluster 2

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https://doi.org/10.1101/2020.04.24.058933

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C C C C C A C A T

T T C T C A C G T

C C T C C A C A C

C C T C T G T A C

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a bC

241T

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C87

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1 2 3 4 5 6 7 8 91 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 91 2 3 4 5 6 7 8 9

T T C T C A C G T

C C C C C A C A T

C C T C T G T A C

C C T C C A C A C


https://doi.org/10.1101/2020.04.24.058933

18060T 23403G 28144C

14408T 17747T 17858G

241T 3037T 8782T

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CCCA TTTG CAC TGT

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countrySouthKoreaChinaJapanUSACanadaAustraliaSpainUKGermanyNetherlandAustriaPortugalBelgiumItalyFranceSwitzerlandBrazil

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https://doi.org/10.1101/2020.04.24.058933

0.0

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COL other mutation sitesthe 9 specific sitesa b


https://doi.org/10.1101/2020.04.24.058933

●

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●R = 0.44 , p = 0.07904

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●R = 0.42 , p = 0.09164

28144T TTTG CAC TTCTCACGT(H1)

17747C 17858A 18060C 23403G

241T 3037T 8782C 14408T

0.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.000.00 0.25 0.50 0.75 1.00

0.04

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Frequency of 9 specific sites and haplotypes

Est

imat

ed d

eath

rat

e


https://doi.org/10.1101/2020.04.24.058933

Ka (mean) 10-3 s.e. (Ka)10-3 Ks (mean) 10-3 s.e. (Ks)10-3 Ka/Ks (mean) s.e. (Ka/Ks) Hypothesis P-value*

SARS-CoV 0.985 0.018 1.310 0.049 0.760 0.038 Ka/Ks ( SARS-CoV < SARS-CoV-2) 0.007

MERS-CoV 1.319 0.040 4.887 0.096 0.260 0.003 Ka/Ks ( MERS-CoV < SARS-CoV-2) 0.000

SARS-CoV-2 0.231 0.004 0.265 0.005 1.008 0.020

*One-sided T-test for the means of two independent samples.

Table 1. Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of the SARS-CoV, MERS-CoV and SARS-CoV-2 genome sequences


https://doi.org/10.1101/2020.04.24.058933

substitution rate (10-3) 95% CI (10-3) tMRCA 95% CI references

SARS-CoV1.05 0.49, 1.65 2002-6-28 2002/1/19, 2002/11/3 this study

- 0.80, 2.38 spring of 2002 - 26

MERS-CoV1.52 1.39, 1.63 2012-6-7 2012/6/4, 2012/6/9 this study

1.12 0.88; 1.37 2012-3 2011/12, 2012/6 27

SARS-CoV-2 1.6 1.42, 1.80 2019-9-27 2019/8/28, 2019/10/26 this study

26 Zhao, Z. et al. Moderate mutation rate in the SARS coronavirus genome and its implications. BMC Evol Biol 4, 21, doi:10.1186/1471-2148-4-21 (2004).

27 Cotten, M. et al. Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus. mBio 5, doi:10.1128/mBio.01062-13 (2014).

Table 2. Substitution rate and tMRCA estimated by BEAST v2.6.2


https://doi.org/10.1101/2020.04.24.058933

Pos Ref AltCluster 1 Cluster 2 Cluster 3

Fst Gene region

Mutation type

Protein changed

codon changed Impact

pi Major allelefrequency pi Major allelefrequency pi Major allelefrequency

241 C T 0.0000 C 1.0000 0.0109 C 0.9945 0.0000 T 1.0000 0.991240 5'UTR upstream NA NA MODIFIER

3037 C T 0.0000 C 1.0000 0.0109 C 0.9945 0.0000 T 1.0000 0.991240 gene-orf1ab synonymous 924F 2772ttC>ttT LOW

8782 C T 0.0000 T 1.0000 0.3863 C 0.7390 0.0000 C 1.0000 0.582113 gene-orf1ab synonymous 2839S 8517agC>agT LOW

14408 C T 0.0000 C 1.0000 0.0055 C 0.9973 0.0000 T 1.0000 0.995609 gene-orf1ab missense 4715P>L 14144cCt>cTt MODERATE

17747 C T 0.0735 T 0.9620 0.0000 C 1.0000 0.0000 C 1.0000 0.975245 gene-orf1ab missense 5828P>L 17483cCt>cTt MODERATE

17858 A G 0.0497 G 0.9747 0.0000 A 1.0000 0.0000 A 1.0000 0.983573 gene-orf1ab missense 5865Y>C 17594tAt>tGt MODERATE

18060 C T 0.0000 T 1.0000 0.0164 C 0.9918 0.0000 C 1.0000 0.976100 gene-orf1ab synonymous 5932L 17796ctC>ctT LOW

23403 A G 0.0000 A 1.0000 0.0164 A 0.9918 0.0000 G 1.0000 0.986895 gene-S missense 614D>G 1841gAt>gGt MODERATE

28144 T C 0.0000 C 1.0000 0.3915 T 0.7335 0.0000 T 1.0000 0.578530 gene-ORF8 missense 84L>S 251tTa>tCa MODERATE

Table 3. The information of the 9 specific sites in each Cluster.


https://doi.org/10.1101/2020.04.24.058933

Ka (mean) 10-3 s.e. (Ka)10-3 Ks (mean) 10-3 s.e. (Ks)10-3 Ka/Ks (mean) s.e. (Ka/Ks) Hypothesis P-value*(Ka/Ks)

H1 0.510 0.057 0.498 0.056 1.099 0.010 Ka/Ks ( H3 < H1) 0.000

H2 0.347 0.042 0.334 0.044 1.268 0.023 Ka/Ks ( H3 < H2) 0.000

H3 0.298 0.007 0.397 0.009 0.795 0.010 - -

H4 0.334 0.023 0.414 0.019 0.796 0.019 - -

*One-sided T-test for the means of two independent samples.

Table 4. Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of each 4 main haplotype genomes


https://doi.org/10.1101/2020.04.24.058933

Documents

Evolution and molecular characteristics of SARS-CoV-2 genome · genome of SARSCoV-2 - in order to develop a comprehensive understanding of the virus and provide a basis for designing