Evolution and molecular characteristics of SARS-CoV-2 ...preearth.net/pdfs/evolution-molecular-covid.pdf · 1 Evolution and molecular characteristics of SARS-CoV-2 2 genome 3 Yunmeng

Evolution and molecular characteristics of SARS-CoV-2 1

genome 2

Yunmeng Bai1#, Dawei Jiang1#, Jerome R Lon1#, Xiaoshi Chen1, Meiling Hu1, Shudai 3

Lin1, Zixi Chen1, Xiaoning Wang1,2, Yuhuan Meng3*, Hongli Du1* 4

1School of Biology and Biological Engineering, South China University of Technology, 5

Guangzhou 510006, China 6

2State Clinic Center of Gearitic, Chinese PLA General Hospital, Beijing 100853, 7

China 8

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

510330, China 10

# Equal contribution. 11

* Corresponding authors. 12

E-mail: [email protected](Du HL), [email protected](Meng YH) 13

14

There are 6 figures, 4 tables, 4 supplementary figures and 6 supplementary tables. 15

16

Abstract 17

In the evolution analysis of 622 or 3624 complete SARS-CoV-2 genomes, the 18

estimated Ka/Ks ratio is 1.008 or 1.094, which is higher than that of SARS-CoV and 19

MERS-CoV, and the time to the most recent common ancestor (tMRCA) is inferred in 20

late September 2019, indicating that SARS-CoV-2 may have completed positive 21

selection pressure of the cross-host evolution in the early stage. Further we find 9 key 22

specific sites of highly linkage which play a decisive role in the classification of 23

SARS-CoV-2. Notably the frequencies of haplotype TTTG and H1 are generally high 24

in European countries and correlated to death rate (r>0.37) based on 3624 or 16373 25

SARS-CoV-2 genomes, which indicated that the haplotypes might be related to 26

pathogenicity of SARS-CoV-2. The development trends in chronological order and 27

Ka/Ks ratio of each major haplotype subgroup showed the four major haplotype 28

.CC-BY-NC-ND 4.0 International licensewas not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (whichthis version posted May 15, 2020. . https://doi.org/10.1101/2020.04.24.058933doi: bioRxiv preprint

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subgroups were going through different evolution patterns, the H3 haplotype 29

subgroup was going through a purifying evolution and almost disappeared after 30

detection, while H1 haplotype subgroup was going through a neutral evolution and 31

globally increased with time. The evolution and molecular characteristics of more 32

than 16000 genomic sequences provided a new perspective for revealing 33

epidemiology of SARS-CoV-2 and coping with SARS-CoV-2 effectively. 34

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

36

Introduction 37

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

serious, public health concern worldwide. To date, a total of seven types of 39

coronaviruses that can infect humans have been found, among which the more typical 40

ones are SARS-CoV, MERS-CoV and SARS-CoV-2. They spread across species and 41

usually cause mild to severe respiratory diseases. The total number of SARS-CoV and 42

MERS-CoV infections are only 8,069 and 2,494 with reproduction number (R0) 43

fluctuates 2.5-3.9 and 0.3-0.8, respectively. However, SARS-CoV-2 has infected 44

2471136 people in 212 countries up to April 22th(1), with the basic R0 ranging from 45

1.4 to 6.49(2). Among these three typical coronavirus, MERS-CoV has the highest 46

death rate of 34.40%, SARS-CoV has the modest death rate of 9.59%, SARS-CoV-2 47

is about 6.99% and 7.69% death rate of the global and Wuhan, but is quite high death 48

rate in some countries of Europe, such as Belgium, Italy, United Kingdom, 49

Netherlands, Spain and France, which even reaches 14.95%, 13.39%, 13.48%, 50

11.61%, 10.42% and 13.60% respectively according to the data of April 22th, 2020. 51

Except for the shortage of medical supplies, aging and other factors, it is not clear 52

whether there is virus mutation effect in these countries with such a significantly 53

increased death rate. 54

It has been reported that SARS-CoV-2 belongs to beta-coronavirus and is mainly 55

transmitted by the respiratory tract, which belongs to the same subgenus 56


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(SarbeCoVirus) as SARS-CoV(3). Through analyzing the genome and structure of 57

SARS-CoV-2, its receptor-binding domain (RBD) was found to bind with 58

angiotensin-converting enzyme 2 (ACE2), which is also one of the receptors for 59

binding SARS-CoV(4). Some early genomic studies have shown that SARS-CoV-2 is 60

similar to certain bat viruses (RaTG13, with the whole genome homology of 96.2%(5)) 61

and Malayan pangolins coronaviruses (GD/P1L and GDP2S, with the whole genome 62

homology of 92.4%(6)). They have speculated several possible origins of 63

SARS-CoV-2 based on its spike protein characteristics (cleavage sites or the 64

RBD)(5-7), in particular, SARS-CoV-2 exhibits 97.4% amino acid similarity to the 65

Guangdong pangolin coronaviruses in RBD, even though it is most closely related to 66

bat coronavirus RaTG13 at the whole genome level. However, it is not enough to 67

present genome-wide evolution by a single gene or local evolution of RBD, whether 68

bats or pangolins play an important role in the zoonotic origin of SARS-CoV-2 69

remains uncertain(8). Furthermore, since there is little molecular characteristics 70

analysis of SARS-CoV-2 based on a large number of genomes, it is difficult to 71

determine whether the virus has significant variation that affects its phenotype. 72

Thereby, it is necessary to further reveal the phylogenetic evolution and molecular 73

characteristics of the whole genome of SARS-CoV-2 in order to develop a 74

comprehensive understanding of the virus and provide a basis for designing vaccines 75

and antiviral treatment strategies. Fortunately, with the increase of SARS-CoV-2 76

genome data, we have an opportunity to reveal phylogenetic evolution and molecular 77

characteristics of SARS-CoV-2 more comprehensively. In this study, the Ka/Ks ratio, 78

substitution rate and the time to the most recent common ancestor (tMRCA) of 79

SARS-CoV-2, SARS-CoV and MERS-CoV were analyzed using KaKs_Calculator 80

and BEAST, and then 622 SARS-CoV-2 genome sequences with high quality were 81

clustered using maximum likelihood (ML) with bootstrap of 100 to ensure the 82

reliability of evolutionary trees. According to the clusters of phylogenetic trees, the 83

characteristic mutations of SARS-CoV-2 were screened and their potential 84


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significance were explored. The results of this study will provide a landscape for us to 85

further understand SARS-CoV-2, and supply a possible basis for the prevention and 86

treatment of SARS-CoV-2 in different areas. 87

Results 88

Genome sequences 89

We got a total of 1053 genomic sequences up to March 22th, 2020. According to the 90

filter criteria, 37 sequences with ambiguous time, 314 with low quality and 78 with 91

similarity of 100% were removed. A total of 624 sequences were obtained to perform 92

multiple sequences alignment. Two highly divergent sequences (EPI_ ISL_414690, 93

EPI_ISL_415710) according to the firstly constructed phylogenetic tree were also 94

filtered out (Table S1). The remaining 622 sequences were used to reconstruct a 95

phylogenetic tree. In addition, total of 3624 and 16373 genomic sequences were 96

redownloaded up to April 6th, 2020 and May 10th, 2020 respectively for further 97

exploring the evolution and molecular characteristics of SARS-CoV-2. 98

Estimate of evolution rate and the time to the most recent common ancestor for 99

SARS-CoV, MERS-CoV, and SARS-CoV-2 100

In this study, date for SARS-CoV-2 ranged from 2019/12/26 to 2020/03/18 was 101

collected. The average Ka/Ks of all the coding sequences was closer to 1 (1.008), 102

which indicating the genome was going through a neutral evolution. We also 103

reevaluated the Ka/Ks of SARS-CoV and MERS-CoV through the whole period, and 104

found the ratio were smaller than SARS-CoV-2 (Table 1). To estimate more credible 105

Ka/Ks for SARS-CoV-2, we recalculated it using redownloaded 3624 genome 106

sequences ranged from 2019/12/26 to 2020/04/06, and the average Ka/Ks of it was 107

1.094 (Table 1), which was almost same with the above result. 108

We assessed the temporal signal using TempEst v1.5.3(9). All three datasets 109

exhibit a positive correlation between root-to-tip divergence and sample collecting 110

time (Figure S1), hence they are suitable for molecular clock analysis in BEAST(9, 111

10). The substitution rate of SARS-CoV-2 genome was estimated to be 1.601 x 10-3 112


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(95% CI: 1.418 x 10-3 - 1.796 x 10-3, Table 2, Figure S2A) substitution/site/year, 113

which is as in the same order of magnitude as SARS-CoV and MERS-CoV. The 114

tMRCA was inferred on the late September, 2019 (95% CI: 2019/08/28- 115

2019/10/26,Table 2, Figure S2B), about 2 months before the early cases of 116

SARS-CoV-2(11). 117

Phylogenetic tree and clusters of SARS-CoV-2 118

The no-root phylogenetic trees constructed by the maximum likelihood method with 119

PhyML 3.1(12) and MEGA(13) were showed in Figure 1, Figure S3A and 3B. 120

According to the shape of phylogenetic trees, we divided 622 sequences into three 121

clusters: Cluster 1 including 76 sequences mainly from North America, Cluster 2 122

including 367 sequences from all regions of the world, and Cluster 3 including 179 123

sequences mainly from Europe (Table S2). 124

The specific sites of each Cluster 125

The Fst and population frequency of a total of 9 sites (NC_045512.2 as reference 126

genome) were detected (Table 3, Table S3). Thereinto, three (C17747T, A17858G and 127

C18060T) are the specific sites of Cluster 1, and four (C241T, C3037T, C14408T and 128

A23403G) are the specific sites of Cluster 3. Notably, C241T was located in the 129

5’-UTR region and the others were located in coding regions (6 in ofr1ab gene, 1 in S 130

gene and 1 in ORF8 gene). Five of them were missense variant, including C14408T, 131

C17747T and A17858G in ofr1ab gene, A23403G in S gene, and C28144T in ORF8 132

gene. The PCA results showed that these 9 specific sites could clearly separate the 133

three Clusters, while all SNV dataset could not clearly separate Cluster 1 and Cluster 134

2 (Figure S4), which further suggested that these 9 specific sites are the key sites for 135

separating the three Clusters. 136

Linkage of specific sites 137

We found the 9 specific sites are highly linkage based on 622 genome sequences 138

(Figure 2A), then we carried out a further linkage analysis using the 3624 genome 139

sequences (Figure 2B). As a result, for the 3624 genome sequences, 3 specific sites in 140


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the Cluster 1 were almost complete linkage, and haplotype CAC and TGT accounted 141

for 98.65% of all the 3 site haplotypes. The same phenomenon was also found in 4 142

specific sites of Cluster 3, and haplotype CCCA and TTTG accounted for 97.68% of 143

all the 4 site haplotypes. Intriguingly, the 9 specific sites were still highly linkage, and 144

four haplotypes, including TTCTCACGT (H1), CCCCCACAT (H2), CCTCTGTAC 145

(H3) and CCTCCACAC (H4), accounted for 95.89% of all the 9 site haplotypes. 146

Thereinto, H1 and H3 had completely different bases at the 9 specific sites. The 147

frequencies of each site and major haplotype for each country were showed in Figure 148

3 and Table S4. The data showed that the haplotype TTTG of the 4 specific sites in 149

Cluster 3 had existed globally at present, and still exhibited high frequencies in most 150

European countries but quite low in Asian countries. While the haplotype TGT of the 151

3 specific sites in Cluster 1 existed almost only in North America and Australia. For 152

the 9 specific sites, most countries had only two or three major haplotypes, except 153

America and Australia had all of four major haplotypes with higher frequency. 154

Characteristics of major haplotype subgroups 155

All haplotypes of 9 specific sites for 3624 or 16373 genomes and the numbers of them 156

were shown in Table S5. Four major haplotypes H1, H2, H3 and H4, three minor 157

haplotypes H5, H7 and H8 close to H1 and one minor haplotype H6 close to H3 were 158

found in both datasets. The numbers of these haplotypes for 16373 genomes with 159

clear collection data detected in each country in chronological order were shown in 160

Figure 4A. From these results, H2 and H4 haplotype subgroups had existed for a long 161

time (2019-12-24 to 2020-04-28), and the H3 haplotype subgroup almost disappeared 162

after detection (2020-02-18 to 2020-04-28), while H1 haplotype subgroup was 163

globally increasing with time (2020-02-18 to 2020-05-05), which indicated H1 164

subgroup had adapted to the human hosts, and was under an adaptive growth period 165

worldwide. However, due to the nonrandom sampling on early phase (only patients 166

having a recent travel to Wuhan were detected), some earlier cases of H3 may be lost, 167

the high proportion of H3 subgroup during February 18 to March 10 could confirm 168


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this point. 169

H3 and H4 subgroups had the lowest Ka/Ks ratio in 3624 or 16373 genomes among 170

the 4 major subgroups (Table 4), suggesting that H3 and H4 subgroup might be going 171

through a purifying evolution and have existed for a long time, while H2 and H1 172

subgroup might be going through a positive selection and a neutral evolution 173

respectively according to their Ka/Ks ratios (Table 4), which were consistent with the 174

above phenomenon that only H1 subgroup was expanding with time around the world. 175

From the whole genome mutations in each major haplotype subgroup (Figure 4B, 176

Table S6), we found that except the 9 specific sites, there was no common mutations 177

with frequencies more than 0.05 in these haplotype subgroups but between H2 and H4 178

subgroups. 179

Phylogenetic network of haplotype subgroups 180

Phylogenetic networks were inferred with 697 mutations called from 3624 genomes 181

dataset. For these datasets, the network structures of TCS and MSN were similar. The 182

major haplotype subgroups H4 and H2 were in the middle of the network, while H1 183

and H3 were in the end nodes of the network (Figure 5). According to the 184

phylogenetic networks, we proposed four hypothesis: (1) the ancestral haplotypes 185

evolved in four directions to obtain H1, H2, H3 and H4 respectively or evolved in two 186

or more directions to obtain two or more major haplotypes and then involved into the 187

other major haplotype(s); (2) H2 or H4 evolved in two directions respectively and 188

finally generated H1 and H3; (3) H1 evolved in one direction to generate H2 and H4, 189

and then evolved into H3; (4) H3 evolved in one direction to generate H4 and H2, and 190

then evolved into H1. Although we cannot exclude the first hypothesis based on the 191

present data, but if there are evolutionary transformation relationships between the 192

four major haplotypes, according to the Ka/Ks, population size and development trend 193

in chronological order of each major haplotype subgroup, we speculate that the most 194

likely transformation hypothesis is that H3 is the ancestral haplotype, which is 195

gradually eliminated with selection, while H4 and H2 is the transitional haplotype in 196


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the evolution process, and H1 may be the haplotype to be finally fixed. 197

Correlation analysis of specific sites with death rate 198

To explore the relationship between death rate and the 9 specific sites, we used 199

Pearson method to calculate the correlation coefficient between death rate and 200

frequency of each specific site or major haplotype in 17 countries with 3624 genomes 201

at early stage. As a result, all r values of 241T, 3037T, 14408T, 23403G and haplotype 202

TTTG and H1 were more than 0.4 (Figure 6, Table S4). We also evaluated the 203

correlation coefficient with 16373 genomes in 30 countries, the r values of haplotype 204

TTTG and H1 were still more than 0.37 (Table S4), which might be increased when 205

the death rates of some countries were further stabilized with time. These finding 206

integrating their high frequencies in most European countries indicated that the 4 sites 207

and haplotype TTTG and H1 might be related to the pathogenicity of SARS-CoV-2. 208

209

Discussion 210

SARS-CoV-2 poses a great threat to the production, living and even survival of 211

human beings(14). With the further outbreak of SARS-CoV-2 in the world, further 212

understanding on the evolution and molecular characteristics of SARS-CoV-2 based 213

on a large number of genome sequences will help us cope better with the challenges 214

brought by SARS-CoV-2. In the present study, we estimated the Ka/Ks ratio and 215

tMRCA of SARS-CoV-2, SARS-CoV and MERS-CoV, and constructed the 216

phylogenetic tree of SARS-CoV-2 genomes. As a result, we found that 9 specific sites 217

of highly linkage could play a decisive role in the classification of SARS-CoV-2, and 218

the haplotype TTTG might be related to pathogenicity of SARS-CoV-2 based on more 219

than 16000 genomes. All these results could accelerate our further understanding of 220

SARS-CoV-2. 221

Exploring evolution rate, tMRCA and phylogenetic tree of SARS-CoV-2 would 222

help us better understand the virus(15). The average Ka/Ks for all the coding 223

sequences of 622 and 3624 SARS-CoV-2 genomes was 1.008 and 1.094, which was 224


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higher than those of SARS-CoV and MERS-CoV, indicating that the SARS-CoV-2 225

was going through a neutral evolution. In general, SARS-CoV would show purifying 226

selection pressures and the Ka/Ks would go down at the middle or end of the 227

epidemic(16). Therefore, it seemed to be reasonable considering that SARS-CoV-2 228

was under an exponential growth period around the world. Interestingly, we also 229

found that the subgroups of different haplotypes (H1, H2, H3 and H4) seemed to 230

experience different evolutionary patterns according to their Ka/Ks. The H3 haplotype 231

subgroup disappeared soon after detection (2020-02-18 to 2020-04-07, Figure 4A), 232

while H1 haplotype subgroup was globally increasing with time, these evolution and 233

changes should be considered in developing therapeutic drugs and vaccines as soon as 234

possible. The tMRCA of SARS-CoV-2 was inferred on the late September, 2019 (95% 235

CI: 2019/08/28- 2019/10/26), about 2 months before the early cases of 236

SARS-CoV-2(11). We also estimated tMRCA of SARS-CoV and MERS-CoV with 237

the same methods, both were about 3 months later than the corresponding tMRCAs 238

estimated by previous studied(17, 18), which indicated the tMRCA of SARS-CoV-2 239

we estimated was reasonable. A recent study used TreeDater method to estimate 240

tMRCA for more than 7000 SARS-CoV-2 genomes and indicated the tMRCA of 241

SARS-CoV-2 was around 6 October 2019 to 11 December 2019, which were about 242

one and a half months later than ours and in broad agreement with six previous 243

studies all performed on smaller subsets of the SARS-CoV-2 genomes with BEAST 244

method(19). While we used the most common method BEAST to estimate tMRCA of 245

SARS-CoV-2 based on 622 genomes, indicating that the estimated tMRCA are 246

reliable. 247

A recent study clustered 160 SARS-CoV-2 whole-genome sequences into A, B 248

and C groups by a phylogenetic network analysis(20). The cluster result was similar 249

to our study: both the samples in Cluster A and our Cluster 1 were mainly from the 250

United States; the samples in Cluster C and our Cluster 3 were mainly from European 251

countries, while Cluster B and our Cluster 2 were mainly from China and the other 252


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regions. It was interesting that the markers in Cluster B (T8782C and C28144T) were 253

also found in our study, but the other markers in Cluster A (T29095C) and Cluster C 254

(G26144T) were not significantly in our study. That may be caused by different 255

sample sizes and different constructing methods of phylogenetic tree. Based on the 256

base substitution model, the ML method avoids the possible "long-branch attraction" 257

problem in the maximum parsimony method and is faster than the Bayesian 258

method(21), hence it could be used as a reliable method for phylogenetic analysis. 259

Nowadays, there is still a shortage of studies on phylogenetic analysis of 260

SARS-CoV-2. Some studies used the genome of bat SARS-like-CoV(22), RaTG13(22) 261

or MT019529 from Wuhan (https://bigd.big.ac.cn/ncov/tree) as the root of 262

phylogenetic tree. Unfortunately, there was no obvious evidence showing that 263

SARS-CoV-2 was from the bat coronavirus even the identity between SARS-CoV-2 264

and RaTG13 was up to 96.2%(5). In our study, the tMRCA of SARS-CoV-2 was 265

inferred on the late September 2019, which indicated there might exist an earlier 266

SARS-CoV-2 strain we didn't find. Then in the case of unclear source of 267

SARS-CoV-2 and high homology of its genomes (> 99.9% homology mostly), it may 268

be inappropriate to identify the evolutionary characteristics inside the genomes by 269

taking bat SARS-like-CoV, RaTG13 or MT019529 as root. Therefore, the maximum 270

likelihood method was used in current study to construct a no-root tree to obtain the 271

reliable clusters with different characteristics. Based on the no-root tree, we identified 272

9 specific sites of highly linkage that play a decisive role in the classification of 273

clusters successfully. Among the four major haplotypes, H1 and H3 were in Cluster 3 274

and Cluster 1, respectively, while there were two haplotypes H2 and H4 in Cluster 2 275

(Figure 1, Figure S3B). 276

Among the 9 specific sites, 8 of them were located in coding regions (6 in ofr1ab 277

gene, 1 in S gene and 1 in ORF8 gene), and 5 of them were missense variant, 278

including C14408T, C17747T and A17858G in ofr1ab gene, A23403G in S gene, and 279

C28144T in ORF8 gene. Orf1ab is composed of two partially overlapping open 280


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reading frames (orf), orf1a and 1b. It is proteolytic cleaved into 16 non-structural 281

proteins (nsp), including nsp1 (suppress antiviral host response), nsp9 (RNA/DNA 282

binding activity), nsp12 (RNA-dependent RNA polymerase), nsp13 (helicase) and 283

others(23), indicating the vital role of it in transcription, replication, innate immune 284

response and virulence(24). C14408T with high frequencies of T in European 285

countries were located at nsp12 region, indicating that this missense variant might 286

influence the role of RNA polymerase. Spike glycoprotein, the largest structural 287

protein on the surface of coronaviruses, comprises of S1 and S2 subunits which 288

mediating binding the receptor on the host cell surface and fusing membranes, 289

respectively(25). It has been reported that S protein of SARS-CoV-2 can bind 290

angiotensin-converting enzyme 2 (ACE2) with higher affinity than that of SARS(4, 5). 291

Whether the missense variant of A23403G in S gene, which with high frequencies of 292

G in European countries, will change the affinity between S protein and ACE2 293

remains to be further investigated. 294

It seems to take a long time to fix mutations finally according to the mutation 295

frequency of each subgroup. For example, H2 and H4 subgroups, which have been 296

detected for more than four months from December 24, 2019 to May 5, 2020 (Figure 297

4A), have more mutations with higher frequencies, but the highest mutation frequency 298

is only 0.486 at the position of 11083 (Figure 4B, Table S6). From these phenomena, 299

it can be inferred that it takes a long time for the specific sites of each major subgroup 300

to be fixed, but it may be faster if the early population is small. In addition, there is 301

also the possibility that an ancestor strain evolved in four directions by obtaining the 302

specific mutations directly and produced the four current major haplotypes, so the 303

evolution time may be shorter, which seems to be consistent with the phenomenon 304

that four major haplotypes can be detected in two months (Figure 4A). However, if 305

there exist transformation evolution pattern among the major haplotypes of 306

SARS-CoV-2, it is difficult to complete the transformation among the four major 307

haplotypes within two months at the current evolution rate of each major haplotype 308


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population. Therefore, we speculate that the transformation between the four major 309

haplotypes may have been completed for a long time, which have not been detected. 310

Previous studies showed that the death rate of SARS-CoV-2 is affected by many 311

factors, such as medical supplies, aging, life style and other factors(26, 27). However, 312

our study found that the 4 specific sites (C241T, C3037T, C14408T and A23403G) in 313

the Cluster 3 were almost complete linkage, and the frequency of haplotype TTTG 314

was generally high in European countries and correlated to death rate (r>0.37) based 315

on 3624 or 16373 SARS-CoV-2 genomes, which provides a new perspective to the 316

reasons of relatively high death rate in Europe. In order to make frequency relatively 317

credible, we selected countries with more than 10 genomes (except South Korea with 318

16 genomes, all the other countries with more than 20 genomes) to analyze the 319

correlation between frequencies of haplotypes and death rate. Therefore, the 320

correlation between the frequencies of haplotypes and death rate obtained in this study 321

is quite reliable, and these specific sites should be considered in designing new 322

vaccine and drug development of SARS-CoV-2. 323

Conclusion and prospective 324

The Ka/Ks ratio of SARS-CoV-2 and tMRCA of SARS-CoV-2 (95% CI: 2019/08/28- 325

2019/10/26) indicated that SARS-CoV-2 might have completed the positive selection 326

pressure of cross-host evolution in the early stage and be going through a neutral 327

evolution at present. The 9 specific sites with highly linkage were found to play a 328

decisive role in the classification of clusters. Thereinto, 3 of them are the specific sites 329

of Cluster 1 mainly from North America and Australia, 4 of them are the specific sites 330

of Cluster 3 mainly from Europe in the early stage, both of these 3 and 4 specific sites 331

are almost complete linkage. The frequencies of haplotype TTTG for the 4 specific 332

sites and H1 for 9 specific sites were generally high in European countries and 333

correlated to death rate (r>0.37) based on 3624 or 16373 SARS-CoV-2 genomes, 334

which suggested these haplotypes might relate to pathogenicity of SARS-CoV-2 and 335

provided a new perspective to the reasons of relatively high death rates in Europe. 336


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The relationship between the haplotype TTTG or H1 and the pathogenicity of 337

SARS-CoV-2 needs to be further verified by clinical samples or virus virulence test 338

experiment, and the 9 specific sites with highly linkage may provide a starting point 339

for the traceability research of SARS-CoV-2. Given that the different evolution 340

patterns of different haplotypes subgroups, we should consider these evolution and 341

changes in the development of therapeutic drugs and vaccines as soon as possible. 342

343

Materials and methods 344

Genome sequences 345

The complete genome sequences of SARS-CoV-2 were downloaded from China 346

National Center for Bioinformation (https://bigd.big.ac.cn/ncov/release_genome/) and 347

GISAID (https://www.gisaid.org/) up to March 22th, 2020. The sequences were 348

filtered out according to the following criteria: (1) sequences with ambiguous time; (2) 349

sequences with low quality which contained the counts of unknown bases > 15 and 350

degenerate bases > 50 (https://bigd.big.ac.cn/ncov/release_genome); (3) sequences 351

with similarity of 100% were removed to unique one. Finally, 624 high quality 352

genomes with precise collection time were selected and aligned using MAFFT v7 353

with automatic parameters. Besides, the genome sequences of 7 SARS-CoV and 475 354

MERS-CoV were also downloaded from NCBI (https://www.ncbi.nlm.nih.gov/), and 355

the MERS-CoV dataset including samples collected from both human and camel. In 356

addition, for further exploring evolution and molecular characteristics of 357

SARS-CoV-2 based on the larger amount of genomic data, we redownloaded 358

validation datasets of the genome sequences from GISAID up to April 6th, 2020 and 359

May 10th, 2020 respectively. 360

Estimate of evolution rate and the time to the most recent common ancestor for 361

SARS-CoV, MERS-CoV, and SARS-CoV-2 362

The average Ka, Ks and Ka/Ks for all coding sequences were calculated using 363

KaKs_Calculator v1.2(28), and the substitution rate and tMRCA were estimated using 364


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BEAST v2.6.2(10). The temporal signal with root-to-tip divergence was visualized in 365

TempEst v1.5.3(9) using a ML whole genome tree with bootstrap value as input. For 366

SARS-CoV and SARS-CoV-2, we selected a strict molecular clock and Coalescent 367

Exponential Population Model. For MERS-CoV, we selected a relaxed molecular 368

clock and Birth Death Skyline Serial Cond Root Model. We used the tip dates and 369

chose the HKY as the site substitution model in all these analyses. Markov Chain 370

Monte Carlo (MCMC) chain length was set to 10,000,000 steps sampling after every 371

1000 steps. The output was examined in Tracer v1.6 372

(http://tree.bio.ed.ac.uk/software/tracer/). 373

Variants calling of SARS-CoV-2 genome sequences 374

Each genome sequence was aligned to the Wuhan-Hu-1 reference genome 375

(NC_045512.2) using bowtie2 with default parameters(29), and variants were called 376

by samtools (sort; mpileup -gf) and bcftoots (call -vm). The merge VCF files were 377

created by bgzip and bcftools (merge --missing-to-ref)(30, 31). 378

Phylogenetic tree construction and virus isolates clustering for SARS-CoV-2 379

After alignment of 624 SARS-CoV-2 genomes with high quality and manually deleted 380

2 highly divergent genomes (EPI_ISL_415710, EPI_ISL_414690) according to the 381

firstly constructed phylogenetic tree, the aligned dataset of 622 sequences was 382

phylogenetically analyzed. The SMS method was used to select GTR+G as the base 383

substitution model(32), and the PhyML 3.1(12) and MEGA(13) were used to 384

construct the no-root phylogenetic tree by the maximum likelihood method with the 385

bootstrap value of 100. The online tool iTOL(33) was used to visualized the 386

phylogenetic tree. The clusters were defined by the shape of phylogenetic tree. 387

Detection of specific sites from each Cluster 388

Information (ID, countries/regions and collection time) and variants (NC_045512.2 as 389

reference genome) of each genome from each Cluster were extracted. The allele 390

frequency and nucleotide divergency (pi) for each site in the virus population of each 391

Cluster were measured by vcftools(34). The Fst were also calculated by vcftools(34) 392


https://doi.org/10.1101/2020.04.24.058933


to assess the diversity between the Clusters. Sites with high level of Fst together with 393

different major allele in each Cluster were filtered as the specific sites. PCA were 394

analyzed by the GCTA v1.93.1beta(35) with the specific sites and all SNV dataset 395

respectively. 396

Linkage analysis of specific sites and characteristics of major haplotype 397

subgroups 398

The linkage disequilibrium of the specific sites were analyzed by haploview(36), and 399

the statistics of the haplotype of the specific sites for each Cluster or country were 400

used in-house perl script. 401

Phylogenetic network of haplotype subgroups 402

The phylogenetic networks were inferred by PopART package v1.7.2(37) using TCS 403

and minimum spanning network (MSN) methods respectively. 404

Frequencies of specific sites or haplotypes and correlation with death rate 405

The frequencies of specific sites for each country were calculated. The death rate was 406

estimated with Total Deaths /Confirmed Cases based on the data from Johns Hopkins 407

resources on May 12th, 2020 408

(https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467409

b48e9ecf6).The correlation coefficient between death rate and frequencies of specific 410

site or haplotype in different countries was calculated using Pearson method. 411

412

Authors’ contributions 413

HD conceived the study. YM, YB, DJ, JL and XC carried out the data analysis and 414

wrote the manuscript. MH, SL, and ZC collected data and revised the manuscript. 415

XW attended the discussions. HD and YM supervised the whole work and revised the 416

manuscript. 417

418

Competing interests 419

The authors declare no competing interests. 420


https://doi.org/10.1101/2020.04.24.058933


421

Acknowledgements 422

This work was supported by the National Key R&D Program of China 423

(2018YFC0910201), the Key R&D Program of Guangdong Province 424

(2019B020226001), the Science and the Technology Planning Project of Guangzhou 425

(201704020176 and 2020Q-P013). 426

427

References 428

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Nucleic Acids Res. 2019;47(W1):W256-W9. 501 34. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format 502 and VCFtools. Bioinformatics (Oxford, England). 2011;27(15):2156-8. 503 35. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait 504 analysis. Am J Hum Genet. 2011;88(1):76-82. 505 36. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype 506 maps. Bioinformatics. 2005;21(2):263-5. 507

37. Leigh JW, Bryant D, Nakagawa S. popart: full ‐ feature software for haplotype network 508

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510

Figure Legends 511

Figure 1 Phylogenetic tree and clusters of 622 SARS-CoV-2 genomes 512

The 622 sequences were clustered into three clusters: Cluster 1 were mainly from 513

North America, Cluster 2 were from regions all over the world, and Cluster 3 were 514

mainly from Europe. 515

Figure 2 Linkage disequilibrium plot of haplotypes of the 9 specific sites 516

A. The plot for 622 genome sequences; B. The plot for 3624 genome sequences. 517

Figure 3 The frequencies of both the 9 specific sites and haplotypes 518

The frequencies of the 9 specific sites (A) and haplotypes (B) in each country for 519

3624 genomes. 520

Figure 4 The characteristics of haplotype subgroups 521

A. The numbers of haplotypes of the 9 specific sites for 16373 genomes with clear 522

collection data detected in each country in chronological order; B. The whole genome 523

mutations in each major haplotype subgroup. 524

Figure 5 Phylogenetic network of haplotype subgroups for 3624 genomes 525

The network was inferred by POPART using TCS method. Each colored vertex 526

represents a haplotype, with different colors indicating the different sampling areas. 527

Hatch marks along edge indicated the number of mutations. Small black circles within 528

the network indicated unsampled haplotypes. H1-H5 subgroups were point out 529

according haplotypes of the 9 specific sites, and other small subgroups were not 530

specially pointed out. 531


https://doi.org/10.1101/2020.04.24.058933


Figure 6 The correlation between death rate and frequencies of both the 9 532

specific sites and haplotypes 533

534

Tables 535

Table 1 Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of the 536

SARS-CoV, MERS-CoV and SARS-CoV-2 genome sequences 537

Table 2 Substitution rate and tMRCA estimated by BEAST v2.6.2 538

Table 3 The information of the 9 specific sites in each Cluster 539

Table 4 Statistics of Ka, Ks and Ka/Ks ratios for all coding regions of each 4 540

major haplotype subgroup with 3624 and 16373 genomes respectively 541

542

Supplementary material 543

Figure S1 Regression analyses of the root-to-tip divergence with the 544

maximum-likelihood phylogenetic tree of SARS-CoV (A), MERS-CoV (B) and 545

SARS-CoV-2 (C) 546

The root-to-tip genetic distances were inferred in TempEst v1.5.3. Three data sets 547

exhibit a positive correlation between root-to-tip divergence and sample collecting 548

time, and they appear to be suitable for molecular clock analysis. 549

Figure S2 Substitution rate and tMRCA estimate of SARS-CoV-2 550

A. Estimated substitution rate of SARS-CoV-2 using BEAST v2.6.2 under a strict 551

molecular clock. The mean rate was 1.601 x 10-3 (substitutions per site per year). B. 552

The estimated tMRCA of SARS-CoV-2 using BEAST v2.6.2 under a strict molecular 553

clock. The mean tMRCA was 2019.74 (date: 2019-09-27). 554

Figure S3 Phylogenetic tree of 622 SARS-CoV-2 genomes 555

A. Phylogenetic tree constructed by PhyML with bootstrap value of 100; B. Phylogenetic tree 556

constructed by MEGA with bootstrap value of 100. 557


https://doi.org/10.1101/2020.04.24.058933


Figure S4 The PCAs analysis result based on the 9 specific sites (A) and all SNVs 558

(B) 559

Table S1 The information of the first downloaded complete genome sequences 560

Table S2 The sequence IDs in each Cluster 561

Table S3 The details information of the SNVs in all Clusters 562

Table S4 The information of death rate, frequencies of the 9 specific sites and 563

major haplotypes in each country for 3624 and 16373 genomes respectively 564

Table S5 All haplotypes of the 9 specific sites and numbers of them for 3624 and 565

16373 genomes respectively 566

Table S6 Whole genome mutations and frequencies in each major haplotype 567

subgroup of 3624 and 16373 genomes respectively 568


https://doi.org/10.1101/2020.04.24.058933


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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

_L

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

_0

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

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D 4.0 International license

was not certified by peer review

) is the author/funder. It is made available under a

The copyright holder for this preprint (w

hichthis version posted M

ay 15, 2020. .

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

bioRxiv 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).

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

bioRxiv 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 0.991240 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 MODERA

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 MODERA

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 MODERA

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 MODERA

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 MODERA

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

bioRxiv 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.

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ay 15, 2020. .

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

bioRxiv preprint

https://doi.org/10.1101/2020.04.24.058933


Documents

Evolution and molecular characteristics of SARS-CoV-2 ...preearth.net/pdfs/evolution-molecular-covid.pdf · 1 Evolution and molecular characteristics of SARS-CoV-2 2 genome 3 Yunmeng