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Comprehensive evolution and molecular characteristics of a large number of 1

SARS-CoV-2 genomes revealed its epidemic trend and possible origins 2

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Yunmeng Bai1#, Dawei Jiang1#, Jerome R Lon1#, Xiaoshi Chen1, Meiling Hu1, Shudai Lin1, Zixi Chen1, 4

Xiaoning Wang1,2, Yuhuan Meng3*, Hongli Du1* 5

1School of Biology and Biological Engineering, South China University of Technology, Guangzhou 6

510006, China 7

2State Clinic Center of Gearitic, Chinese PLA General Hospital, Beijing 100853, China 8

3Guangzhou KingMed Transformative Medicine Institute Co., Ltd, Guangzhou 510330, China 9

# Equal contribution. 10

* Corresponding authors. 11

E-mail: [email protected], [email protected] 12

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Abstract 14

Objectives: 15

To reveal epidemic trend and possible origins of SARS-CoV-2 by exploring its evolution and 16

molecular characteristics based on a large number of genomes since it has infected millions of 17

people and spread quickly all over the world. 18

Methods: 19

Various evolution analysis methods were employed. 20

Results: 21

.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted June 30, 2020. . https://doi.org/10.1101/2020.04.24.058933doi: bioRxiv preprint

https://doi.org/10.1101/2020.04.24.058933

http://creativecommons.org/licenses/by-nc-nd/4.0/

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The estimated Ka/Ks ratio of SARS-CoV-2 is 1.008 or 1.094 based on 622 or 3624 SARS-CoV-2 22

genomes, and the time to the most recent common ancestor (tMRCA) was inferred in late 23

September 2019. Further 9 key specific sites of highly linkage and four major haplotypes H1, H2, 24

H3 and H4 were found. The Ka/Ks, detected population size and development trends of each 25

major haplotype showed H3 and H4 subgroups were going through a purify evolution and almost 26

disappeared after detection, indicating H3 and H4 might have existed for a long time, while H1 27

and H2 subgroups were going through a near neutral or neutral evolution and globally increased 28

with time. Notably the frequency of H1 was generally high in Europe and correlated to death rate 29

(r>0.37). 30

Conclusions: 31

In this study, the evolution and molecular characteristics of more than 16000 genomic sequences 32

provided a new perspective for revealing epidemiology of SARS-CoV-2. 33

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KEYWORDS: SARS-CoV-2; evolution; classification; haplotype 35

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Introduction 37

The global outbreak of SARS-CoV-2 is currently and increasingly recognized as a serious, public 38

health concern worldwide. Coronaviruses exist widely around the world, and to date, a total of seven 39

types of coronaviruses that can infect humans have been found, of which four coronaviruses including 40

hCoV-229E, hCoV-NL63, hCoV-OC43 and hCoV-HKU1 cause a cold, while the other three including 41

SARS-CoV, MERS-CoV and SARS-CoV-2 usually cause mild to severe respiratory diseases. The total 42


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number of SARS-CoV and MERS-CoV infections are only 8,069 and 2,494 with reproduction number 43

(R0) fluctuates 2.5-3.9 and 0.3-0.8, respectively. However, SARS-CoV-2 has infected 2471136 people 44

in 212 countries up to April 22th(WHO, 2020), with the basic R0 ranging from 1.4 to 6.49(Liu, et al., 45

2020). Among these three typical coronavirus, MERS-CoV has the highest death rate of 34.40%, 46

SARS-CoV has the modest death rate of 9.59%, SARS-CoV-2 is about 6.99% and 7.69% death rate of 47

the global and Wuhan, but is quite high death rate in some countries of Europe, such as Belgium, Italy, 48

United Kingdom, Netherlands, Spain and France, which even reaches 14.95%, 13.39%, 13.48%, 49

11.61%, 10.42% and 13.60% respectively according to the data of April 22th, 2020. Except for the 50

shortage of medical supplies, aging and other factors, it is not clear whether there is virus mutation 51

effect in these countries with such a significantly increased death rate. 52

It has been reported that SARS-CoV-2 belongs to beta-coronavirus and is mainly transmitted by the 53

respiratory tract, which belongs to the same subgenus (SarbeCoVirus) as SARS-CoV(Lu, et al., 2020). 54

Through analyzing the genome and structure of SARS-CoV-2, its receptor-binding domain (RBD) was 55

found to bind with angiotensin-converting enzyme 2 (ACE2), which is also one of the receptors for 56

binding SARS-CoV(Wrapp, et al., 2020). Some early genomic studies have shown that SARS-CoV-2 is 57

similar to certain bat viruses (RaTG13, with the whole genome homology of 96.2%(Zhou, et al., 2020)) 58

and Malayan pangolins coronaviruses (GD/P1L and GDP2S, with the whole genome homology of 59

92.4%(Lam, et al., 2020)). They have speculated several possible origins of SARS-CoV-2 based on its 60

spike protein characteristics (cleavage sites or the RBD)(Lam, et al., 2020, Zhang and Holmes, 2020, 61

Zhou, et al., 2020), in particular, SARS-CoV-2 exhibits 97.4% amino acid similarity to the Guangdong 62

pangolin coronaviruses in RBD, even though it is most closely related to bat coronavirus RaTG13 at 63


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the whole genome level. However, it is not enough to present genome-wide evolution by a single gene 64

or local evolution of RBD, whether bats or pangolins play an important role in the zoonotic origin of 65

SARS-CoV-2 remains uncertain(Andersen, et al., 2020). Furthermore, since there is little molecular 66

characteristics analysis of SARS-CoV-2 based on a large number of genomes, it is difficult to 67

determine whether the virus has significant variation that affects its phenotype. Thereby, it is necessary 68

to further reveal the phylogenetic evolution and molecular characteristics of the whole genome of 69

SARS-CoV-2 in order to develop a comprehensive understanding of the virus and provide a basis for 70

the prevention and treatment of SARS-CoV-2. 71

72

Results 73

Genome sequences 74

We got a total of 1053 genomic sequences up to March 22th, 2020. According to the filter criteria, 37 75

sequences with ambiguous time, 314 with low quality and 78 with similarity of 100% were removed. A 76

total of 624 sequences were obtained to perform multiple sequences alignment. Two highly divergent 77

sequences (EPI_ ISL_414690, EPI_ISL_415710) according to the firstly constructed phylogenetic tree 78

were also filtered out (Table S1). The remaining 622 sequences were used to reconstruct a phylogenetic 79

tree. In addition, total of 3624 and 16373 genomic sequences were redownloaded up to April 6th, 2020 80

and May 10th, 2020 respectively for further exploring the evolution and molecular characteristics of 81

SARS-CoV-2. 82


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Estimate of evolution rate and the time to the most recent common ancestor for SARS-CoV, 83

MERS-CoV, and SARS-CoV-2 84

In this study, date for SARS-CoV-2 ranged from 2019/12/26 to 2020/03/18 was collected. The average 85

Ka/Ks of all the coding sequences was closer to 1 (1.008), which indicating the genome was going 86

through a neutral evolution. We also reevaluated the Ka/Ks of SARS-CoV and MERS-CoV through the 87

whole period, and found the ratio were smaller than SARS-CoV-2 (Table 1). To estimate more credible 88

Ka/Ks for SARS-CoV-2, we recalculated it using redownloaded 3624 genome sequences ranged from 89

2019/12/26 to 2020/04/06, and the average Ka/Ks of it was 1.094 (Table 1), which was almost same 90

with the above result. 91

We assessed the temporal signal using TempEst v1.5.3(Rambaut, et al., 2016). All three datasets 92

exhibit a positive correlation between root-to-tip divergence and sample collecting time (Figure S1), 93

hence they are suitable for molecular clock analysis in BEAST(Bouckaert, et al., 2019, Rambaut, et al., 94

2016). The substitution rate of SARS-CoV-2 genome was estimated to be 1.601 x 10-3 (95% CI: 1.418 x 95

10-3 - 1.796 x 10-3, Table 2, Figure S2A) substitution/site/year, which is as in the same order of 96

magnitude as SARS-CoV and MERS-CoV. The tMRCA was inferred on the late September, 2019 (95% 97

CI: 2019/08/28- 2019/10/26,Table 2, Figure S2B), about 2 months before the early cases of 98

SARS-CoV-2(Huang, et al., 2020). 99

Phylogenetic tree and clusters of SARS-CoV-2 100

The no-root phylogenetic trees constructed by the maximum likelihood method with PhyML 101

3.1(Guindon, et al., 2010) and MEGA(Kumar, et al., 2018) were showed in Figure 1, Figure S3A and 102

3B. According to the shape of phylogenetic trees, we divided 622 sequences into three clusters: Cluster 103


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1 including 76 sequences mainly from North America, Cluster 2 including 367 sequences from all 104

regions of the world, and Cluster 3 including 179 sequences mainly from Europe (Table S2). 105

The specific sites of each Cluster 106

The Fst and population frequency of a total of 9 sites (NC_045512.2 as reference genome) were 107

detected (Table 3, Table S3). Thereinto, three (C17747T, A17858G and C18060T) are the specific sites 108

of Cluster 1, and four (C241T, C3037T, C14408T and A23403G) are the specific sites of Cluster 3. 109

Notably, C241T was located in the 5’-UTR region and the others were located in coding regions (6 in 110

ofr1ab gene, 1 in S gene and 1 in ORF8 gene). Five of them were missense variant, including C14408T, 111

C17747T and A17858G in ofr1ab gene, A23403G in S gene, and T28144C in ORF8 gene. The PCA 112

results showed that these 9 specific sites could clearly separate the three Clusters, while all SNV 113

dataset could not clearly separate Cluster 1 and Cluster 2 (Figure S4), which further suggested that 114

these 9 specific sites are the key sites for separating the three Clusters. 115

Linkage of specific sites 116

We found the 9 specific sites are highly linkage based on 622 genome sequences (Figure 2A), then we 117

carried out a further linkage analysis using the 3624 genome sequences (Figure 2B). As a result, for the 118

3624 genome sequences, 3 specific sites in the Cluster 1 were almost complete linkage, and haplotype 119

CAC and TGT accounted for 98.65% of all the 3 site haplotypes. The same phenomenon was also 120

found in 4 specific sites of Cluster 3, and haplotype CCCA and TTTG accounted for 97.68% of all the 121

4 site haplotypes. Intriguingly, the 9 specific sites were still highly linkage, and four haplotypes, 122

including TTCTCACGT (H1), CCCCCACAT (H2), CCTCTGTAC (H3) and CCTCCACAC (H4), 123

accounted for 95.89% of all the 9 site haplotypes. Thereinto, H1 and H3 had completely different bases 124


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at the 9 specific sites. The frequencies of each site and major haplotype for each country were showed 125

in Figure 3 and Table S4. The data showed that the haplotype TTTG of the 4 specific sites in Cluster 3 126

had existed globally at present, and still exhibited high frequencies in most European countries but 127

quite low in Asian countries. While the haplotype TGT of the 3 specific sites in Cluster 1 existed 128

almost only in North America and Australia. For the 9 specific sites, most countries had only two or 129

three major haplotypes, except America and Australia had all of four major haplotypes and with 130

relative higher frequencies. 131

Characteristics and epidemic trends of major haplotype subgroups 132

All haplotypes of 9 specific sites for 3624 or 16373 genomes and the numbers of them were shown in 133

Table S5. Four major haplotypes H1, H2, H3 and H4, three minor haplotypes H5, H7 and H8 close to 134

H1 and one minor haplotype H6 close to H3 were found in both datasets. The numbers of these 135

haplotypes for 16373 genomes with clear collection data detected in each country in chronological 136

order were shown in Figure 4A. From these results, H2 and H4 haplotype subgroups had existed for a 137

long time (2019-12-24 to 2020-04-28), the detected population size of H2 subgroup was far greater 138

than that of H4 subgroup, and the H3 haplotype subgroup almost disappeared after detection 139

(2020-02-18 to 2020-04-28), while H1 haplotype subgroup was globally increasing with time 140

(2020-02-18 to 2020-05-05), which indicated H1 subgroup had adapted to the human hosts, and was 141

under an adaptive growth period worldwide. However, due to the nonrandom sampling on early phase 142

(only patients having a recent travel to Wuhan were detected), some earlier cases of H3 may be lost, the 143

high proportion of H3 subgroup during February 18 to March 10 could confirm this point. 144

H3 and H4 subgroups had the lowest Ka/Ks ratio in 3624 or 16373 genomes among the 4 major 145


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subgroups (Table 4), suggesting that H3 and H4 subgroups might be going through a purifying 146

evolution and have existed for a long time, while H2 and H1 subgroups might be going through a near 147

neutral or neutral evolution according to their Ka/Ks ratios (Table 4), which were consistent with the 148

above phenomenon that only H1 and H2 subgroups were expanding with time around the world. From 149

the whole genome mutations in each major haplotype subgroup (Figure 4B, Table S6), we found that 150

except the 9 specific sites, there was no common mutations with frequencies more than 0.05 in these 151

haplotype subgroups but between H2 and H4 subgroups. 152

Phylogenetic network of haplotype subgroups 153

Phylogenetic networks were inferred with 697 mutations called from 3624 genomes dataset. For these 154

datasets, the network structures of TCS and MSN were similar. The major haplotype subgroups H4 and 155

H2 were in the middle of the network, while H1 and H3 were in the end nodes of the network (Figure 156

5). According to the phylogenetic networks, we proposed four hypothesis: (1) the ancestral haplotypes 157

evolved in four directions to obtain H1, H2, H3 and H4 respectively or evolved in two or more 158

directions to obtain two or more major haplotypes and then involved into the other major haplotype(s); 159

(2) H2 or H4 evolved in two directions respectively and finally generated H1 and H3; (3) H1 evolved 160

in one direction to generate H2 and H4, and then evolved into H3; (4) H3 evolved in one direction to 161

generate H4 and H2, and then evolved into H1. Although we cannot exclude the first hypothesis based 162

on the present data, but if there are evolutionary relationships among the four major haplotypes, 163

according to the Ka/Ks, detected population size and development trends in chronological order of each 164

major haplotype subgroup, we speculate that the most likely evolution hypothesis is that H3 and H4 are 165

the earliest haplotypes, which is gradually eliminated with selection, while H2 is the transitional 166


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haplotype in the evolution process, and H1 may be the haplotype to be finally fixed. 167

Correlation analysis of specific sites with death rate and infectivity 168

To explore the relationship between death rate and the 9 specific sites, we used Pearson method to 169

calculate the correlation coefficient between death rate and frequency of each specific site or major 170

haplotype in 17 countries with 3624 genomes at early stage. As a result, all r values of 241T, 3037T, 171

14408T, 23403G and haplotype TTTG and H1 were more than 0.4 (Figure 6, Table S4). We also 172

evaluated the correlation coefficient with 16373 genomes in 30 countries, the r values of haplotype 173

TTTG and H1 were still more than 0.37 (Table S4), which might be increased when the death rates of 174

some countries were further stabilized with time. These finding integrating their high frequencies in 175

most European countries indicated that the 4 sites and haplotype TTTG and H1 might be related to the 176

pathogenicity of SARS-CoV-2. 177

To explore the relationship between infectivity and the 9 specific sites, we used population size of 178

major haplotypes to deduce the possible specific sites related infectivity. We assumed that these major 179

haplotype in each country were subject to similar virus transmission and control patterns, while the 180

population sizes of H1 and H2 subgroups were far greater than those of H3 and H4 subgroups (Figure 181

4A, Table S4), then the common different specific sites of H1 and H2 subgroups with H3 and H4 182

subgroups, C8782T and T28144C, might be related to the infectivity of SARS-CoV-2, and the virus 183

with C8782 and T28144 might be more infectious than those virus with 8782T and 28144C. 184

185

Discussion 186

SARS-CoV-2 poses a great threat to the production, living and even survival of human beings(Guo, et 187


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al., 2020). With the further outbreak of SARS-CoV-2 in the world, further understanding on the 188

evolution and molecular characteristics of SARS-CoV-2 based on a large number of genome sequences 189

will help us cope better with the challenges brought by SARS-CoV-2. 190

Exploring evolution rate, tMRCA and phylogenetic tree of SARS-CoV-2 would help us better 191

understand the virus(Yuen, et al., 2020). The average Ka/Ks for all the coding sequences of 622 and 192

3624 SARS-CoV-2 genomes was 1.008 and 1.094, which was higher than those of SARS-CoV and 193

MERS-CoV, indicating that the SARS-CoV-2 was going through a neutral evolution. In general, 194

SARS-CoV would show purifying selection pressures and the Ka/Ks would go down at the middle or 195

end of the epidemic(He, et al., 2004). Therefore, it seemed to be reasonable considering that 196

SARS-CoV-2 was under an exponential growth period around the world. Interestingly, we also found 197

that the subgroups of different haplotypes (H1, H2, H3 and H4) seemed to experience different 198

evolutionary patterns according to their Ka/Ks. The H3 haplotype subgroup disappeared soon after 199

detection (2020-02-18 to 2020-04-28, Figure 4A), while H1 haplotype subgroup was globally 200

increasing with time, these evolution and changes should be considered in developing therapeutic drugs 201

and vaccines. The tMRCA of SARS-CoV-2 was inferred on the late September, 2019 (95% CI: 202

2019/08/28- 2019/10/26), about 2 months before the early cases of SARS-CoV-2(Huang, et al., 2020). 203

We also estimated tMRCA of SARS-CoV and MERS-CoV with the same methods, both were about 3 204

months later than the corresponding tMRCAs estimated by previous studied(Cotten, et al., 2014, Zhao, 205

et al., 2004). A recent study used TreeDater method to estimate tMRCA for more than 7000 206

SARS-CoV-2 genomes and indicated the tMRCA of SARS-CoV-2 was around 6 October 2019 to 11 207

December 2019, which were about one and a half months later than ours and in broad agreement with 208


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six previous studies all performed on no more than 120 early SARS-CoV-2 genomes with BEAST 209

method(van Dorp, et al., 2020). While we used the most common method BEAST to estimate tMRCA 210

of SARS-CoV-2 based on 622 genomes and combined with the comparison of tMRCA of SARS-CoV 211

and MERS-CoV, indicating that the estimated tMRCA in the present study are more reliable. 212

A recent study clustered 160 SARS-CoV-2 whole-genome sequences into A, B and C groups by a 213

phylogenetic network analysis by taking bat RaTG13 as root (Forster, et al., 2020). The cluster result 214

was similar to our study: both the samples in Cluster A and our Cluster 1 were mainly from the United 215

States; the samples in Cluster C and our Cluster 3 were mainly from European countries, while Cluster 216

B and our Cluster 2 were mainly from China and the other regions. It was interesting that the markers 217

C8782T and T28144C, which were also discovered by Yu et al(Yu, et al., 2020) , in Cluster B were 218

also found in our study, but the other markers in Cluster A (T29095C) and Cluster C (G26144T) were 219

not significantly in our study. That may be caused by different sample sizes and different constructing 220

methods of phylogenetic tree. Based on the base substitution model, the ML method avoids the 221

possible "long-branch attraction" problem in the maximum parsimony method and is faster than the 222

Bayesian method(Holder and Lewis, 2003), hence it could be used as a reliable method for 223

phylogenetic analysis. Some studies used the genome of bat SARS-like-CoV(Zhang, et al., 2020), 224

RaTG13(Zhang, et al., 2020) or MT019529 (https://bigd.big.ac.cn/ncov/tree) as the root of 225

phylogenetic tree. Unfortunately, there was no obvious evidence showing that SARS-CoV-2 was from 226

the bat coronavirus even the identity between SARS-CoV-2 and RaTG13 was up to 96.2%(Zhou, et al., 227

2020). In our study, the tMRCA of SARS-CoV-2 was inferred on the late September 2019, which 228

indicated there might exist an earlier SARS-CoV-2 strain we didn't find. Then in the case of unclear 229


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source of SARS-CoV-2 and high homology of its genomes (> 99.9% homology mostly), it may be 230

inappropriate to identify the evolutionary characteristics inside the genomes by taking bat 231

SARS-like-CoV, RaTG13 or MT019529 as root. Therefore, the maximum likelihood method was used 232

in current study to construct a no-root tree to obtain the reliable clusters with different characteristics. 233

Based on the no-root tree, we identified 9 specific sites of highly linkage that play a decisive role in the 234

classification of clusters successfully. Among the four major haplotypes, H1 and H3 were in Cluster 3 235

and Cluster 1, respectively, while there were two haplotypes H2 and H4 in Cluster 2 (Figure 1, Figure 236

S3B). 237

Among the 9 specific sites, 8 of them were located in coding regions (6 in ofr1ab gene, 1 in S gene 238

and 1 in ORF8 gene), and 5 of them were missense variant, including C14408T, C17747T and 239

A17858G in ofr1ab gene, A23403G in S gene, and C28144T in ORF8 gene. Orf1ab is composed of 240

two partially overlapping open reading frames (orf), orf1a and 1b. It is proteolytic cleaved into 16 241

non-structural proteins (nsp), including nsp1 (suppress antiviral host response), nsp9 (RNA/DNA 242

binding activity), nsp12 (RNA-dependent RNA polymerase), nsp13 (helicase) and others(Chan, et al., 243

2020), indicating the vital role of it in transcription, replication, innate immune response and 244

virulence(Graham, et al., 2008). C14408T with high frequencies of T in European countries were 245

located at nsp12 region, indicating that this missense variant might influence the role of RNA 246

polymerase. Spike glycoprotein, the largest structural protein on the surface of coronaviruses, 247

comprises of S1 and S2 subunits which mediating binding the receptor on the host cell surface and 248

fusing membranes, respectively(Li, 2016). It has been reported that S protein of SARS-CoV-2 can bind 249

angiotensin-converting enzyme 2 (ACE2) with higher affinity than that of SARS(Wrapp, et al., 2020, 250


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Zhou, et al., 2020). Whether the missense variant of A23403G in S gene, which with high frequencies 251

of G in European countries, will change the affinity between S protein and ACE2 remains to be further 252

investigated. 253

It seems to take a long time to finally fix mutations according to the mutation frequency of each 254

subgroup. For example, H2 and H4 subgroups, which have been detected for more than four months 255

from December 24, 2019 to May 5, 2020 (Figure 4A), have more mutations with higher frequencies, 256

but the highest mutation frequency is only 0.486 at the position of 11083 (Figure 4B, Table S6). From 257

these phenomena, it can be inferred that it takes a long time for the specific sites of each major 258

subgroup to be fixed, but it may be faster if the early population is small. In addition, there is also the 259

possibility that an ancestor strain evolved in four directions by obtaining the specific mutations directly 260

and produced the four current major haplotypes, so the evolution time for obtaining four major 261

haplotypes may be shorter, which seems to be consistent with the phenomenon that four major 262

haplotypes can be detected in two months (Figure 4A). However, if there exist evolution relationship 263

among the major haplotypes of SARS-CoV-2, it is difficult to complete the evolution among the four 264

major haplotypes within two months (2019-12-24 to 2020-02-18, Figure 4A) at the current evolution 265

rate of each major haplotype population (Table 4). Therefore, we speculate that the transformation 266

among the four major haplotypes may have been completed for a long time, which have not been 267

detected. What's interesting is that only United States and Australia among 29 countries had all of four 268

major haplotypes and with relative higher frequency (Figure 4A, Table S4), which indicated that the 269

two countries are the most likely places where the virus appeared earlier based on all the present data. 270

Previous studies showed that the death rate of SARS-CoV-2 is affected by many factors, such as 271


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medical supplies, aging, life style and other factors(Liu, et al., 2020, Malavolta, et al., 2020). However, 272

our study found that the 4 specific sites (C241T, C3037T, C14408T and A23403G) in the Cluster 3 273

were almost complete linkage, and the frequency of haplotype TTTG was generally high in European 274

countries and correlated to death rate (r>0.37) both based on 3624 or 16373 SARS-CoV-2 genomes, 275

which provides a new perspective to the reasons of relatively high death rate in Europe, and these 276

specific sites should be considered in designing new vaccine and drug development of SARS-CoV-2. 277

Two possible specific sites C8782T and T28144C related to the infectivity of SARS-CoV-2 were also 278

deduced in the present study, which would provide some basis for SARS-CoV-2 epidemiology. 279

280

Conclusion and prospective 281

The Ka/Ks ratio of SARS-CoV-2 and tMRCA of SARS-CoV-2 (95% CI: 2019/08/28- 2019/10/26) 282

indicated that SARS-CoV-2 might have completed the selection pressure of cross-host evolution in the 283

early stage and be going through a neutral evolution at present. The 9 specific sites with highly linkage 284

were found to play a decisive role in the classification of clusters. Thereinto, 3 of them are the specific 285

sites of Cluster 1 mainly from North America and Australia, 4 of them are the specific sites of Cluster 3 286

mainly from Europe in the early stage, both of these 3 and 4 specific sites are almost complete linkage. 287

The frequencies of haplotype TTTG for the 4 specific sites and H1 for 9 specific sites were generally 288

high in European countries and correlated to death rate (r>0.37) based on 3624 or 16373 SARS-CoV-2 289

genomes, which suggested these haplotypes might relate to pathogenicity of SARS-CoV-2 and 290

provided a new perspective to the reasons of relatively high death rates in Europe. The relationship 291

between the haplotype TTTG or H1 and the pathogenicity of SARS-CoV-2 needs to be further verified 292


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by clinical samples or virus virulence test experiment, and the 9 specific sites with highly linkage may 293

provide a starting point for the traceability research of SARS-CoV-2. Given that the different evolution 294

patterns of different haplotypes subgroups, we should consider these evolution and changes in the 295

development of therapeutic drugs and vaccines. 296

297

Materials and methods 298

Genome sequences 299

The complete genome sequences of SARS-CoV-2 were downloaded from China National Center for 300

Bioinformation (https://bigd.big.ac.cn/ncov/release_genome/) and GISAID (https://www.gisaid.org/) 301

up to March 22th, 2020. The sequences were filtered out according to the following criteria: (1) 302

sequences with ambiguous time; (2) sequences with low quality which contained the counts of 303

unknown bases > 15 and degenerate bases > 50 (https://bigd.big.ac.cn/ncov/release_genome); (3) 304

sequences with similarity of 100% were removed to unique one. Finally, 624 high quality genomes 305

with precise collection time were selected and aligned using MAFFT v7 with automatic parameters. 306

Besides, the genome sequences of 7 SARS-CoV and 475 MERS-CoV were also downloaded from 307

NCBI (https://www.ncbi.nlm.nih.gov/), and the MERS-CoV dataset including samples collected from 308

both human and camel. In addition, for further exploring evolution and molecular characteristics of 309

SARS-CoV-2 based on the larger amount of genomic data, we redownloaded validation datasets of the 310

genome sequences from GISAID up to April 6th, 2020 and May 10th, 2020 respectively. 311


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Estimate of evolution rate and the time to the most recent common ancestor for SARS-CoV, 312

MERS-CoV, and SARS-CoV-2 313

The average Ka, Ks and Ka/Ks for all coding sequences were calculated using KaKs_Calculator 314

v1.2(Zhang, et al., 2006), and the substitution rate and tMRCA were estimated using BEAST 315

v2.6.2(Bouckaert, et al., 2019). The temporal signal with root-to-tip divergence was visualized in 316

TempEst v1.5.3(Rambaut, et al., 2016) using a ML whole genome tree with bootstrap value as input. 317

For SARS-CoV and SARS-CoV-2, we selected a strict molecular clock and Coalescent Exponential 318

Population Model. For MERS-CoV, we selected a relaxed molecular clock and Birth Death Skyline 319

Serial Cond Root Model. We used the tip dates and chose the HKY as the site substitution model in all 320

these analyses. Markov Chain Monte Carlo (MCMC) chain length was set to 10,000,000 steps 321

sampling after every 1000 steps. The output was examined in Tracer v1.6 322

(http://tree.bio.ed.ac.uk/software/tracer/). 323

Variants calling of SARS-CoV-2 genome sequences 324

Each genome sequence was aligned to the reference genome (NC_045512.2) using bowtie2 with 325

default parameters(Langmead and Salzberg, 2012), and variants were called by samtools (sort; mpileup 326

-gf) and bcftoots (call -vm). The merge VCF files were created by bgzip and bcftools (merge 327

--missing-to-ref)(Li, 2011, Li, et al., 2009). 328

Phylogenetic tree construction and virus isolates clustering for SARS-CoV-2 329

After alignment of 624 SARS-CoV-2 genomes with high quality and manually deleted 2 highly 330

divergent genomes (EPI_ISL_415710, EPI_ISL_414690) according to the firstly constructed 331

phylogenetic tree, the aligned dataset of 622 sequences was phylogenetically analyzed. The SMS 332


https://doi.org/10.1101/2020.04.24.058933


17

method was used to select GTR+G as the base substitution model(Lefort, et al., 2017), and the PhyML 333

3.1(Guindon, et al., 2010) and MEGA(Kumar, et al., 2018) were used to construct the no-root 334

phylogenetic tree by the maximum likelihood method with the bootstrap value of 100. The online tool 335

iTOL(Letunic and Bork, 2019) was used to visualized the phylogenetic tree. The clusters were defined 336

by the shape of phylogenetic tree. 337

Detection of specific sites from each Cluster 338

Information (ID, countries/regions and collection time) and variants (NC_045512.2 as reference 339

genome) of each genome from each Cluster were extracted. The allele frequency and nucleotide 340

divergency (pi) for each site in the virus population of each Cluster were measured by 341

vcftools(Danecek, et al., 2011). The Fst were also calculated by vcftools(Danecek, et al., 2011) to 342

assess the diversity between the Clusters. Sites with high level of Fst together with different major 343

allele in each Cluster were filtered as the specific sites. PCA were analyzed by the GCTA 344

v1.93.1beta(Yang, et al., 2011) with the specific sites and all SNV dataset respectively. 345

Linkage analysis of specific sites and characteristics of major haplotype subgroups 346

The linkage disequilibrium of the specific sites were analyzed by haploview(Barrett, et al., 2005), and 347

the statistics of the haplotype of the specific sites for each Cluster or country were used in-house perl 348

script. 349

Phylogenetic network of haplotype subgroups 350

The phylogenetic networks were inferred by PopART package v1.7.2(Leigh, et al., 2015) using TCS 351

and minimum spanning network (MSN) methods respectively. 352


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18

Frequencies of specific sites or haplotypes and correlation with death rate 353

The frequencies of specific sites for each country were calculated. The death rate was estimated with 354

Total Deaths /Confirmed Cases based on the data from Johns Hopkins resources on May 12th, 2020 355

(https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6).The 356

correlation coefficient between death rate and frequencies of specific site or haplotype in different 357

countries was calculated using Pearson method. 358

359

Authors’ contributions 360

HD conceived the study. YM, YB, DJ, JL and XC carried out the data analysis and wrote the 361

manuscript. MH, SL, and ZC collected data and revised the manuscript. XW attended the discussions. 362

HD and YM supervised the whole work and revised the manuscript. 363

Conflict of Interest 364

The authors declare no conflict of interest. 365

Funding sources 366

This work was supported by the National Key R&D Program of China [2018YFC0910201], the Key 367

R&D Program of Guangdong Province [2019B020226001], the Science and the Technology Planning 368

Project of Guangzhou [201704020176 and 2020Q-P013]. 369

Ethical Approval 370

Not required. 371


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372

References 373

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453

Figure Legends 454

Figure 1 Phylogenetic tree and clusters of 622 SARS-CoV-2 genomes 455

The 622 sequences were clustered into three clusters: Cluster 1 were mainly from North America, 456

Cluster 2 were from regions all over the world, and Cluster 3 were mainly from Europe. 457

Figure 2 Linkage disequilibrium plot of haplotypes of the 9 specific sites 458

A. The plot for 622 genome sequences; B. The plot for 3624 genome sequences. 459

Figure 3 The frequencies of both the 9 specific sites and haplotypes 460

The frequencies of the 9 specific sites (A) and haplotypes (B) in each country for 3624 genomes. 461

Figure 4 The characteristics of haplotype subgroups 462

A. The numbers of haplotypes of the 9 specific sites for 16373 genomes with clear collection data 463

detected in each country in chronological order; B. The whole genome mutations in each major 464

haplotype subgroup. 465

Figure 5 Phylogenetic network of haplotype subgroups for 3624 genomes 466

The network was inferred by POPART using TCS method. Each colored vertex represents a haplotype, 467

with different colors indicating the different sampling areas. Hatch marks along edge indicated the 468

number of mutations. Small black circles within the network indicated unsampled haplotypes. H1-H5 469

subgroups were point out according haplotypes of the 9 specific sites, and other small subgroups were 470

not specially pointed out. 471


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22

Figure 6 The correlation between death rate and frequencies of both the 9 specific sites and 472

haplotypes 473

474

Tables 475

Table 1 Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of the SARS-CoV, 476

MERS-CoV and SARS-CoV-2 genome sequences 477

Table 2 Substitution rate and tMRCA estimated by BEAST v2.6.2 478

Table 3 The information of the 9 specific sites in each Cluster 479

Table 4 Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of each 4 major haplotype 480

subgroup with 3624 and 16373 genomes respectively 481

482

Supplementary material 483

Figure S1 Regression analyses of the root-to-tip divergence with the maximum-likelihood 484

phylogenetic tree of SARS-CoV (A), MERS-CoV (B) and SARS-CoV-2 (C) 485

The root-to-tip genetic distances were inferred in TempEst v1.5.3. Three data sets exhibit a positive 486

correlation between root-to-tip divergence and sample collecting time, and they appear to be suitable 487

for molecular clock analysis. 488

Figure S2 Substitution rate and tMRCA estimate of SARS-CoV-2 489

A. Estimated substitution rate of SARS-CoV-2 using BEAST v2.6.2 under a strict molecular clock. The 490

mean rate was 1.601 x 10-3 (substitutions per site per year). B. The estimated tMRCA of SARS-CoV-2 491

using BEAST v2.6.2 under a strict molecular clock. The mean tMRCA was 2019.74 (date: 492


https://doi.org/10.1101/2020.04.24.058933


23

2019-09-27). 493

Figure S3 Phylogenetic tree of 622 SARS-CoV-2 genomes 494

A. Phylogenetic tree constructed by PhyML with bootstrap value of 100; B. Phylogenetic tree 495

constructed by MEGA with bootstrap value of 100. 496

Figure S4 The PCAs analysis result based on the 9 specific sites (A) and all SNVs (B) 497

Table S1 The information of the first downloaded complete genome sequences 498

Table S2 The sequence IDs in each Cluster 499

Table S3 The details information of the SNVs in all Clusters 500

Table S4 The information of death rate, frequencies of the 9 specific sites and major haplotypes 501

in each country for 3624 and 16373 genomes respectively 502

Table S5 All haplotypes of the 9 specific sites and numbers of them for 3624 and 16373 genomes 503

respectively 504

Table S6 Whole genome mutations and frequencies in each major haplotype subgroup of 3624 505

and 16373 genomes respectively 506


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EPI_ISL_408482_2020/01/19_Asia/China/Shandong/Qingdao

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

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EPI_ISL_413589_2020/03/01_Europe/Netherlands/Utrecht

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EPI_ISL_413593_2020/02/29_Europe/Luxembourg

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se

We

nih

Rht

ro

N/y

na

mre

G/e

por

uE

_6

2/2

0/0

20

2_

99

44

14

_L

SI_I

PE

EPI_ISL_414500_2020/03/04_Europe/UnitedKingdom/England/Yorkshire/Sheffield

tcirtsiD

gre

bsni

eH/

aila

hpts

eW

eni

hR

htro

N/yn

amr

eG/

ep

oru

E_

72/

20/

02

02

_5

05

41

4_

LSI

_IP

Etcirt

siD

gre

bsni

eH/

aila

hpts

eW

eni

hR

htro

N/yn

amr

eG/

ep

oru

E_

82/

20/

02

02

_9

05

41

4_

LSI

_IP

E

EPI_IS

L_4145

17_202

0/02/2

5_Asia

/HongK

ong

EPI_ISL_414520_2020/03/02_Europe/Germany/Munich

hci

nu

M/y

na

mre

G/e

por

uE

_2

0/3

0/0

20

2_

12

54

14

_L

SI_I

PE



EPI_IS

L_4145

24_202

0/03/0

1_Euro

pe/Uni

tedKin

gdom/E

ngland

EPI_ISL_

414525_2

020/03/0

2_Europe

/UnitedK

ingdom/E

ngland


EPI_IS

L_4145

27_202

0/02/0

9_Asia

/HongK

ong

EPI_IS

L_4145

28_202

0/02/1

0_Asia

/HongK

ong






EPI_ISL_414549_2020/03/03_Europe/Netherlands/NoordHolland









EPI_IS

L_4145

69_202

0/02/1

2_Asia

/HongK

ong


EPI_ISL_414577_2020/03/02_SouthAmerica/Chile/Talca

EPI_ISL_414578_2020/03/04_SouthAmerica/Chile/Talca

EPI_ISL_414579_2020/03/03_SouthAmerica/Chile/Santiago


EPI_ISL_414586_2020/03/03_Europe/Ireland/Limerick

EPI_ISL_414587_2020/03/03_Europe/Ireland/Limerick

EPI_ISL_414591_2020/03/06_NorthAmerica/USA/Washington/Kirkland

EPI_ISL_414592_2020/03/06_NorthAmerica/USA/Washington/Tacoma




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

EPI_ISL_414601_2020/

02/27_Europe/France/

Normandie/Rouen


EPI_ISL_414617_2020/03/08_NorthAmerica/USAEPI_ISL_414618_2020/03/08_NorthAmerica/USA

EPI_ISL_414620_2020/03/08_NorthAmerica/USA


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

EPI_ISL_414624_2020/02

/26_Europe/France/Norm

andie/Rouen

EPI_ISL_414625_2020/02/26_Europe/France/PaysdelaLoire/Nantes

EPI_ISL_414626_2020/02/29_Europe

/France/HautsdeFrance/CrpyenValo

is

EPI_ISL_414627_2020/03/02_Europe/France/HautsdeFrance/Compigne




EPI_ISL_

414631_2

020/03/0

4_Europe

/France/

Grand-Es

t/Reims

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

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





EPI_ISL_414638_2020/03/04_Europe/Franc

e/HautsdeFrance/Compigne

EPI_ISL_414641_2020/03/05_Europe/Finland



EPI_IS

L_4146

46_202

0/03/0

4_Euro

pe/Fin

land

EPI_IS

L_4146

48_202

0/03/1

1_Nort

hAmeri

ca/USA

/Calif

ornia/

SanDie

goCoun

ty








EPI_ISL_414938_2020/01/24_Asia/China/Shandong



EPI_ISL_415105_2020/03/04_SouthAmerica/Brazil/Bahia/FeiradeSantana

EPI_ISL_

415128_2

020/02/2

9_SouthA

merica/B

razil/Es

piritoSa

nto/Vila

Velha


EPI_ISL_415151_2020/03/04_NorthAmerica/USA/NewYork

EPI_ISL_415153_2020/03/03_Europe/Belgium/Huldenberg

EPI_ISL_415154_2020/03/01_Europe/Belgium/Kraainem



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

EPI_ISL_415158_2020/03/01_Europe/Belgium/Brussels


EPI_ISL_

415435_2

020/03/0

6_Europe

/UnitedK

ingdom/W

ales

EPI_ISL_415454_2020/02/28_Europe/Switzerland

EPI_IS

L_4154

55_202

0/02/2

8_Euro

pe/Swi

tzerla

nd

EPI_IS

L_4154

56_202

0/02/2

9_Euro

pe/Swi

tzerla

nd



EPI_ISL_415459_2020/02/29_Europe/Switzerland/Genve

EPI_ISL_415460_2020/03/09_Europe/Netherlands/Flevoland

EPI_ISL_415461_2020/03/10_Europe/Netherlands/Gelderland

EPI_ISL_415462_2020/03/09_Europe/Netherlands/Gelderland

EPI_ISL_415467_2020/03/10_Europe/Netherlands







EPI_ISL_

415478_2

020/03/0

8_Europe

/Netherl

ands





EPI_ISL_415486_2

020/03/13_Europe

/Netherlands



EPI_ISL_415491

_2020/03/07_Eu

rope/Netherlan

ds

EPI_ISL_4154

92_2020/03/1

0_Europe/Net

herlands

sd

nalr

eht

eN/

ep

oru

E_

01/

30/

02

02

_5

94

51

4_

LSI

_IP

E








EPI_ISL_4155

12_2020/03/0

9_Europe/Net

herlands/Noo

rdBrabant


EPI_ISL_415525_2020/03/12_Europe/Netherlands/NoordHolland

EPI_ISL_415527_2

020/03/12_Europe

/Netherlands/Utr

echt

dn

allo

Hdi

uZ/s

dn

alre

hte

N/e

por

uE

_9

0/3

0/0

20

2_

13

55

14

_L

SI_I

PE




EPI_ISL_415539_2020/03/13_NorthAmerica/USA/Utah



EPI_ISL_415581_2020/03/01_NorthAmerica/Canada/BritishColumbia

EPI_ISL_415583_2020/03/03_NorthAmerica/Canada/BritishColumbia

aib

mul

oC

hsitir

B/a

da

na

C/a

cire

mA

htro

N_

20/

30/

02

02

_4

85

51

4_

LSI

_IP

E






EPI_ISL_415597_2020/03/10_NorthAmerica/USA/Washington/Seattle


EPI_ISL_415600_2020/03/09_NorthAmerica/USA

EPI_ISL_415601

_2020/03/10_No

rthAmerica/USA

/Washington














EPI_ISL_415616_2020/03/08_NorthAmerica/USA/WashingtonEPI_ISL_415617_2020/03/09_NorthAmerica/USA/Washington




EPI_ISL_415622_2020/03/09_NorthAmerica/USA/WashingtonEPI_ISL_415624_2020/03/09_NorthAmerica/USA/Washington




EPI_ISL_415629_2020/03/04_Europe/UnitedKingdom/Scotland

EPI_ISL_

415630_2

020/03/0

9_Europe

/UnitedK

ingdom/S

cotland

EPI_ISL_4156

31_2020/03/1

0_Europe/Uni

tedKingdom/S

cotland

EPI_ISL_415640_2020/03/08_Europe/UnitedKingdom/Scotland

EPI_IS

L_4156

41_202

0/02/2

7_Asia

/Georg

ia/Tbi

lisi

EPI_IS

L_4156

42_202

0/03/1

0_Asia

/Georg

ia/Tbi

lisi

EPI_IS

L_4156

43_202

0/03/1

0_Asia

/Georg

ia/Tbi

lisi

EPI_ISL_415646_2020/03/03_Europe/Italy

EPI_ISL_415648_2020/03/03_Europe/Italy

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

EPI_ISL_415650_2020/03/02_Europe/France/IDF

EPI_ISL_

415651_2

020/03/0

5_Europe

/France/

Bourgogn

e-France

-Comt/Mo

ntreux-C

hateau

EPI_ISL_

415652_2

020/03/0

5_Europe

/France/

Bourgogn

e-France

-Comt/Th

ise


EPI_IS

L_4156

55_202

0/03/1

2_Euro

pe/Uni

tedKin

gdom/W

ales

sel

aW/

mo

dg

niK

deti

nU/

ep

oru

E_

21/

30/

02

02

_7

56

51

4_

LSI

_IP

E





EPI_IS

L_4157

01_202

0/03/0

2_Euro

pe/Swi

tzerla

nd



EPI_ISL_41

5704_2020/

03/04_Euro

pe/Switzer

land

EPI_ISL_41

5705_2020/

03/06_Euro

pe/Switzer

land

EPI_ISL_41

5706_2020/

03/06_Euro

pe/Switzer

land

EPI_ISL_41

5707_2020/

03/08_Euro

pe/Switzer

land

EPI_ISL_415708

_2020/03/07_Eu

rope/Switzerla

nd

EPI_IS

L_4157

09_202

0/01/2

5_Asia

/China

/Hangz

hou

EPI_ISL_415711_2020/01/22_Asia/China/Hangzhou



sel

aW/

mo

dg

niK

deti

nU/

ep

oru

E_

21/

30/

02

02

_0

29

51

4_

LSI

_IP

E

EPI_ISL_416024_2020/03/11_Europe/UnitedKingdom/Wales

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

I_ISL_416029_2020/03/04_SouthAmerica/Brazil/SaoPaulo/SaoPaulo

EPI_ISL_

416031_2

020/03/0

4_SouthA

merica/B

razil/Sa

oPaulo/S

aoPaulo

EPI_ISL_41

6032_2020/

03/04_Sout

hAmerica/B

razil/Dist

ritoFedera

l/Brasilia

olu

aP

oa

S/ol

ua

Po

aS/liz

arB/

acire

mA

htu

oS

_3

0/3

0/0

20

2_

33

06

14

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SI_I

PE

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



uo

hzg

na

H/a

nih

C/ai

sA

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2/1

0/0

20

2_

24

06

14

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SI_I

PE




EPI_ISL_416140_2020/03/02_Europe/Denmark/Copenhagen

EPI_ISL_416141_2020/03/03_Europe/Denmark/Copenhagen

EPI_ISL_416316_2020/01/25_Asia/China/Shanghai









EPI_IS

L_4163

25_202

0/02/0

2_Asia

/China

/Shang

hai






















ia

hg

na

hS/

ani

hC/

ais

A_

10/

20/

02

02

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73

61

4_

LSI

_IP

E





ia

hg

na

hS/

ani

hC/

ais

A_

10/

20/

02

02

_9

73

61

4_

LSI

_IP

E















EPI_ISL_416411_2020/01/25_Oceania/Australia/Victoria/Melbourne




EPI_ISL_41

6426_2020/

03/17_Euro

pe/Hungary

/Budapest

EPI_ISL_416428_2020/03/07_Asia/Vietnam/Quangning

EPI_ISL_416429_2020/02/11_Asia/Vietnam/Vinhphuc

EPI_ISL_416430_2020/03/06_Asia/Vietnam/Hanoi

EPI_ISL_416431_2020/03/06_Asia/Vietnam/Hanoi

EPI_ISL_416432_2020/03/07_Asia/SaudiArabia/Riyadh





















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

EPI_ISL_416458_2020/03/02_Asia/Kuwait/Hawali

ytn

uo

Cg

niK/

not

gni

hsa

W/A

SU/

acire

mA

htro

N_

92/

20/

02

02

_0

64

61

4_

LSI

_IP

Eyt

nu

oC

gni

K/n

otg

nih

sa

W/A

SU/

acir

em

Ahtr

oN

_9

2/2

0/0

20

2_

16

46

14

_L

SI_I

PE

EPI_ISL_416465_2020/02/29_NorthAmerica/USA/Washington/KingCounty

ytn

uo

Cg

niK/

not

gni

hsa

W/A

SU/

acire

mA

htro

N_

30/

30/

02

02

_6

64

61

4_

LSI

_IP

E

EPI_ISL_416467_2020/03/02_Europe/Belgium/Holsbeek


EPI_ISL_416469_2020/03/03_Europe/Belgium/Kessel-Lo

EPI_ISL_416470_2020/03/02_Europe/Belgium/CouthuinEPI_ISL_416471_2020/03/02_Europe/Belgium/Kessel-Lo

EPI_ISL_416472_2020/03/02_Europe/Belgium/Kessel-Lo





EPI_ISL_416477_2020/03/08_Asia/Georgia/Tbilisi



EPI_IS

L_4164

80_202

0/03/1

1_Asia

/Georg

ia/Tbi

lisi

EPI_ISL_4164

81_2020/03/1

6_Asia/Georg

ia/Tbilisi

EPI_IS

L_4164

82_202

0/03/1

3_Asia

/Georg

ia/Tbi

lisi

eiksro

go

nol

eiZ/

dn

alo

P/e

por

uE

_3

0/3

0/0

20

2_

88

46

14

_L

SI_I

PE

EPI_ISL_4164

89_2020/03/1

5_NorthAmeri

can/USA/WI/D

anecounty

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

EPI_ISL_

416493_2

020/03/0

8_Europe

/France/

HautsdeF

rance/Ch

teau-Thi

erry

EPI_ISL_416494_202

0/03/04_Europe/Fra

nce/Normandie/Roue

n



EPI_ISL_416497_2020/03/10_Europe/France/Hautsd

eFrance/Compigne

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

EPI_ISL_

416499_2

020/03/1

1_Europe

/France/

IledeFra

nce/Long

jumeau

EPI_ISL_416502_2020/02/26_Europe/France/Bretagne/Rennes

EPI_IS

L_4165

03_202

0/03/0

1_Euro

pe/Fra

nce/Br

etagne

/Renne

s



EPI_IS

L_4165

07_202

0/03/0

5_Euro

pe/Fra

nce/Br

etagne

/Renne

s







EPI_ISL_416519_2020/03/02_Oceania/NewZealand/Auckland

EPI_ISL_416521_2020/03/10_As

ia/SaudiArabia

Tree scale: 0.0001

Inner Color: Continents

Asia

Europe

NorthAmerica

Oceania

SouthAmerica

Outer Clolors: Countries

Australia

Belgium

Brazil

Cambodia

Canada

Chile

China

Denmark

Finland

France

Georgia

Germany

HongKong_China

Hungary

India

Ireland

Italy

Japan

Kuwait

Luxembourg

Mexico

Nepal

Netherlands

NewZealand

Poland

Portugal

SaudiArabia

Singapore

SouthKorea

Sweden

Switzerland

Taiwan_China

Thailand

UnitedKingdom

UnitedStates

Vietnam

Shape Plot: Haplotype

H1

H2

H3

H4

H5

H6

Other

Cluster 2 Cluster 1

Cluster 3Cluster 2


https://doi.org/10.1101/2020.04.24.058933


1 2 3 4 5 6 7 8 9

9999

9898

99

9898

Block 1 (27 kb)1 2 3 4 5 6 7 8 9

9799

9799

9999

9999

9998

9999

9997

9797

9898

9999

9898

98

98

98

9998

99

Block 1 (27 kb)

.423

.288

.148

.122

.423

.288

.148

.122

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

.454

.324

.123

.063

.454

.324

.123

.063

A BC

241T

C30

37T

C87

82T

C14

408T

C17

747T

A178

58G

C18

060T

A234

03G

T281

44C

C24

1T

C30

37T

C87

82T

C14

408T

C17

747T

A178

58G

C18

060T

A234

03G

T281

44C

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

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

Freq

uenc

y of

eac

h sp

ecifi

c si

te

TTCTCACGT(H1) CCCCCACAT(H2) CCTCTGTAC(H3) CCTCCACAC(H4)

CCCA TTTG CAC TGT

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

Freq

uenc

y of

major

hap

loty

pes

of th

e 4,

3 an

d 9

spec

ific

site

s

countrySouthKoreaChinaJapanUSACanadaAustraliaSpainUKGermanyNetherlandAustriaPortugalBelgiumItalyFranceSwitzerlandBrazil

Asia

NorthAmerica Ocieania

Europe

SouthAmerica

A

B

countrySouthKoreaChinaJapanUSACanadaAustraliaSpainUKGermanyNetherlandAustriaPortugalBelgiumItalyFranceSwitzerlandBrazil

Asia

NorthAmerica Ocieania

Europe

SouthAmerica

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil

Sout

hKor

eaC

hina

Japa

nU

SAC

anad

aAu

stra

liaSp

ain

UK

Ger

man

yN

ethe

rland

Aust

riaPo

rtuga

lBe

lgiu

mIta

lyFr

ance

Switz

erla

ndBr

azil


https://doi.org/10.1101/2020.04.24.058933


0.0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000

H1

COL

1

2

0.0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000

H2

COL

1

2

0.0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000

H3

COL

1

2

0.0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000Pos

H4

COL

1

2

A B COL other mutation sitesthe 9 specific sites


https://doi.org/10.1101/2020.04.24.058933


Australia

Austria

Belgium

Brazil

Canada

China

France

Germany

Italy

Japan

Netherlands

Portugal

South_Korea

Spain

Switzerland

USA

United_Kingdom

H1

H5

H2

H4

H3


https://doi.org/10.1101/2020.04.24.058933


●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

R = 0.46 , p = 0.06451

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

R = 0.18 , p = 0.4799

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

R = 0.33 , p = 0.1906

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

R = 0.47 , p = 0.055

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

R = 0.18 , p = 0.4962

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R = 0.45 , p = 0.06913

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R = 0.32 , p = 0.2105

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R = 0.18 , p = 0.4807

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R = 0.18 , p = 0.4802

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R = 0.44 , p = 0.08004

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R = 0.46 , p = 0.06523

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R = 0.45 , p = 0.07226

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

0.08

0.12

0.16

0.04

0.08

0.12

0.16

0.04

0.08

0.12

0.16

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*

622 genomes

P-value*

3624 genomes

SARS-CoV 0.985 0.018 1.310 0.049 0.760 0.038 Ka/Ks ( SARS-CoV < SARS-CoV-2) 0.139 0.084

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

SARS-CoV-2 622 genomes 0.231 0.004 0.265 0.005 1.008 0.020

3624 genomes 0.287 0.002 0.298 0.002 1.094 0.010

Note: *One-sided Mann-Whitney U-test for the means of two independent samples.

.C

C-B

Y-N

C-N

D 4.0 International license

(which w

as not certified by peer review) is the author/funder. It is m

ade available under aT

he copyright holder for this preprintthis version posted June 30, 2020.

. https://doi.org/10.1101/2020.04.24.058933

doi: bioR

xiv preprint

https://doi.org/10.1101/2020.04.24.058933


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

SARS-CoV 1.050 0.489, 1.654 2002/6/28 2002/1/19, 2002/11/3 this study

- 0.800, 2.380 spring of 2002 - 17

MERS-CoV 1.516 1.392, 1.632 2012/6/7 2012/6/4, 2012/6/9 this study

1.120 0.876, 1.370 2012/3 2011/12, 2012/6 18

SARS-CoV-2 1.601 1.418, 1.796 2019/9/27 2019/8/28, 2019/10/26 this study

17. Zhao Z, Li H, Wu X, Zhong Y, Zhang K, Zhang YP, et al. Moderate mutation rate in the SARS coronavirus genome and its

implications. BMC Evol Biol. 2004;4:21.

18. Cotten M, Watson SJ, Zumla AI, Makhdoom HQ, Palser AL, Ong SH, et al. Spread, circulation, and evolution of the

Middle East respiratory syndrome coronavirus. mBio. 2014;5(1).

.C

C-B

Y-N

C-N


(which w




. https://doi.org/10.1101/2020.04.24.058933

doi: bioR

xiv preprint

https://doi.org/10.1101/2020.04.24.058933


Pos Ref Alt

Cluster 1 Cluster 2 Cluster 3

Fst Gene

region

Mutation

type

Protein

changed

codon

changed Impact

pi Major

allele frequency pi

Major

allele frequency pi

Major

allele frequency

241 C T 0.0000 C 1.0000 0.0109 C 0.9945 0.0000 T 1.0000 0.9912 5'UTR upstream NA NA MODIFIER

3037 C T 0.0000 C 1.0000 0.0109 C 0.9945 0.0000 T 1.0000 0.9912 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 0.5821 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 0.9956 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 0.9752 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 0.9836 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 0.9761 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 0.9869 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 0.5785 gene-ORF8 missense 84L>S 251tTa>tCa MODERATE

.C

C-B

Y-N

C-N


(which w




. https://doi.org/10.1101/2020.04.24.058933

doi: bioR

xiv preprint

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*

3624 genomes

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 - -

16373 genomes

H1 0.416 0.011 0.384 0.011 1.178 0.005 Ka/Ks ( H3 < H1) 0.000

H2 0.332 0.014 0.316 0.014 1.224 0.012 Ka/Ks ( H3 < H2) 0.000

H3 0.305 0.004 0.401 0.005 0.809 0.007 - -

H4 0.354 0.010 0.477 0.009 0.749 0.010 - -

Note: *One-sided Mann-Whitney U-test for the means of two independent samples.

.C

C-B

Y-N

C-N


(which w




. https://doi.org/10.1101/2020.04.24.058933

doi: bioR

xiv preprint

https://doi.org/10.1101/2020.04.24.058933


1 comprehensive evolution and molecular characteristics of ... · 24/04/2020 · in addition, for...

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