Wang et al. Hereditas (2018) 155:19
https://doi.org/10.1186/s41065-018-0057-5

RESEARCH

Open Access

Genetic structure, divergence andadmixture of Han Chinese, Japaneseand Korean populations
Yuchen Wang1,2, Dongsheng Lu1,2, Yeun-Jun Chung3 and Shuhua Xu1,2,4,5,6*

Abstract
Background: Han Chinese, Japanese and Korean, the three major ethnic groups of East Asia, share many similarities
in appearance, language and culture etc., but their genetic relationships, divergence times and subsequent genetic
exchanges have not been well studied.
Results: We conducted a genome-wide study and evaluated the population structure of 182 Han Chinese, 90
Japanese and 100 Korean individuals, together with the data of 630 individuals representing 8 populations wordwide.
Our analyses revealed that Han Chinese, Japanese and Korean populations have distinct genetic makeup and can bewell distinguished based on either the genome wide data or a panel of ancestry informative markers (AIMs). Their
genetic structure corresponds well to their geographical distributions, indicating geographical isolation played a criticalrole in driving population differentiation in East Asia. The most recent common ancestor of the three populations wasdated back to 3000 ~ 3600 years ago. Our analyses also revealed substantial admixture within the three populations
which occurred subsequent to initial splits, and distinct gene introgression from surrounding populations, of which
northern ancestral component is dominant.
Conclusions: These estimations and findings facilitate to understanding population history and mechanism of human
genetic diversity in East Asia, and have implications for both evolutionary and medical studies.
Keywords: Han Chinese, Japanese, Korean, Genetic ancestry, Population structure, Population divergence, Admixture,
SNP

Background
East Asia is one of the world’s most populated places,
consisting of about 38% of the Asian population or
about 22% of the world-wide population. East Asianpeoples, especially the three major ethnicities, Han
Chinese, Japanese and Korean, share many similarities incharacteristics, for example, yellow skin, black eyes andblack hair, short and flat noses, which make them hard
to be distinguished by appearance. Moreover, East Asianpeople use similar languages and words, for example,
Chinese characters are shared in Japanese, and also
* Correspondence: xushua@picb.ac.cn
1Chinese Academy of Sciences (CAS) Key Laboratory of Computational
Biology, Max Planck Independent Research Group on Population Genomics,
CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai
Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai
200031, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
Full list of author information is available at the end of the article

existed in Korean until their recent abolition in the
1940s [1].
While many studies have reported the overall pictureof genetic structure of global populations, finer scaledetails of population structure and relevant issues in
East Asia have not yet been well addressed. In fact, inmany previous studies, samples of Han Chinese andJapanese populations were generally treated as a single
group [2], and notably, Korean samples are frequently
absent from many important international collaborative
projects due to their assumed similarity to Han Chineseand Japanese samples, such as the International HapMap
Project [3] and the 1000 Genomes Project [4]. A recent
study [5] provided a landscape of autosomal variation
and an overall picture of the genetic relationship of
Asian populations. However, fine scale genetic structure
and relationship among Han Chinese, Japanese and© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Wang et al. Hereditas (2018) 155:19

Korean populations have not been revealed or wellstudied, partially due to low density of the data.
Here, we newly genotyped 100 Korean samples, andconducted a joint population genetic analysis with 182
Han Chinese individuals and 90 Japanese individuals.
We attempted to address some fundamental questions
related to the population histories of the three ethnic
groups. First, we asked what the genetic make-up of thethree populations is and how well they are differentiated.
Second, we attempted to dissect population history of
the three ethnic groups and infer their genetic origins
based on state-of-art methods developed recently. Finally, we asked whether there were subsequent geneflow occurred since population splits. We believe these
efforts can advance our understanding of human genetic
diversity and migration history in East Asia, and provide
insightful information for future medical studies in thethree East Asian populations.

Results
Population structure and genetic relationship
Genetic difference measured by FST

To assess the genetic relationship among East Asiangroups, we examined genome-wide single nucleotide
polymorphism (SNP) data in 1032 individuals representing 12 populations. We first calculated pairwise FST basedon genome-wide SNPs (Additional file 1: Figure S1,
Additional file 2: Table S2). The three smallest FST values
are between north Han Chinese (CHB) and south Han
Chinese (CHS) (FST[CHB-CHS] = 0.0014), between Chinese
Dai in Xishuangbanna (CDX) and Kinh in Ho Chi Minh
City of Vietnam (KHV) (FST[CDX-KHV] = 0.0024), and between CHB and Korean (KOR) (FST[CHB-KOR] = 0.0026).
Genetic difference between KOR and Japanese (JPT)(FST[JPT-KOR] = 0.0033) is larger than that between KOR
and CHB, but smaller than that between two Mongolian
populations (Buryat Mongolian (BMON) and DU Mongolian in Qinghai (QHM) [6], FST[BMON-QHM] = 0.0053).
These results are largely consistent with the geographic
distribution of populations. Generally, pairwise FST between Han Chinese, Japanese and Korean (0.0026~ 0.
0090) are greater than that within Han Chinese (0.0014),
but are much smaller than that between European (CEU)
and any East Asian groups, for example, the smallest FST
between CEU and East Asian populations (FST[CEU-BMON]= 0.0838; FST[CEU-KHV] = 0.1059) are higher by an order ofmagnitude. These results suggested a comparatively closer
relationship between Han Chinese, Japanese and Korean.
More detailed information about the populations can befound in Additional file 3: Table S1.
Phylogenetic trees of populations and individuals

A maximum likelihood (ML) tree reconstructed basedon pairwise allele frequency difference provides a better

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visualization of the genetic relationship of populations(Fig. 2a). All the East Asian populations share a clade,and Mongolians are much closer to European thanany other East Asian groups. Among the eight nonMongolian East Asian populations, the Tibetan population (TIB) shows apparent differentiation from the otherseven populations which are comprised of four typicalmainland populations (CDX, CHB, CHS and KHV) andthree island or peninsula populations (JPRK, JPT and
KOR).
An individual level neighbor-joining (NJ) tree of 1002
world-wide individuals based on genotyping differentiation was also constructed (Additional file 4: Figure S2A).
African and European individuals cluster together respectively, and East Asians individuals also have their own
distinct cluster. In East Asians individuals, Mongolian,
Ryukyuan and Tibetan have relatively distinct cluster,while Han Chinese, Japanese and Korean showed arelatively mixed phylogeny, suggest their much closer
relationship, although substructures are also apparent.
Han Chinese samples also show mixture with Southern
populations (CDX and KHV).
Using only 6 populations (two Han Chinese populations,Japanese, Korean and two Mongolian populations) toreconstruct an individual tree, we found the phylogeny ofthe populations became clearer (Additional file 4:
Figure S2B). Japanese individuals have their own cluster
and Korean individuals are almost distinct from Han
Chinese. North and South Han Chinese mixed together,but still have some substructure.
Principal component analysis

We further performed principal components analysis(PCA) to examine the population structure on individuallevel (Figs. 1b, 2b, and Additional file 5: Figure S3). We
first analyzed East Asian populations with populations
worldwide based on 9063 genome-wide SNPs with intermarker distance > 500 kb (see Methods) (Additional file 5:
Figure S3A). The first principal component (PC1) and thesecond principal component (PC2) explain 9.2 and 4.4%of the total variance (or 58.0% and 28.0% of the top ten
PCs), respectively. East Asian groups cluster together
closely and are located far from European or African in
the PC plot, consistent with their differentiation fromother continental populations. We observed some individuals, most of which are Mongolian individuals (BMONor QHM), extend towards CEU cluster, suggesting recent
gene flow might have occurred between European andMongolian populations. Analysis of a dataset with higher
density markers (44,549 SNPs, after excluding JPRK samples due to lower marker density, Additional file 5: Figure
S3B) showed similar pattern except that individuals weremore tightly clustered (PC1 and PC2 explained 10.2% and4.7% of the variance respectively).

Wang et al. Hereditas (2018) 155:19

Page 3 of 12abFig. 1 Asian samples location and population relatedness. (a) Geographic location of the sampled populations (generated by R 2.15.2 and Microsoft
PowerPoint 2010); (b) Principal component analysis (PCA) of all the 10 Asian sample populations

Although East Asian individuals cluster very tightly inthe context of analysis of worldwide populations, PCA
of ten East Asian populations showed they have substantial substructure (Fig. 1b). For example, Han Chinese(CHB and CHS) and southern populations (CDX andKHV) were separated from other populations by PC1.
According to coordinate of PC2 which explained 0.7% of
the total variance, Mongolian and Tibetan are closely located in one side, while the island populations (Japaneseand Ryukyuan) other side. Except Mongolian andTibetan, other populations showed much closer relationship in the PC plot (Fig. 1b, Additional file 5: Figure S3C).
Some Tibetan and DU Mongolian (QHM) individuals distribute toward CHB cluster, suggesting gene flow between
these northern populations. These patterns are even more
obvious when JPRK samples were removed from the analysis (Additional file 5: Figure S3C). High density dataset

showed more pronounced substructure: On a finer scale,
two Mongolian populations also showed substructure, and
QHM individuals are sitting between BMON and TIB
clusters which is consistent with their history [6]. In
all the analyses, Dai (CDX) and Vietnamese (KHV)
always cluster closely and can be separated from Han
Chinese, Japanese and Korean (Fig. 1b, Additional file 5:
Figure S3C).
To examine genetic relationship of the typical East
Asian populations, we did a separate PCA with somesouthern populations excluded (Fig. 2b). PC1 separates
Mongolian from the other groups and explain 0.9% of thevariance (or 21.9% of the top 10 PCs). PC2 separates populations living in the islands and peninsula from the mainland populations. Japanese and Korean each has eachindependent cluster, while two Han Chinese populations
and two Mongolian both have some overlaps, and some

Wang et al. Hereditas (2018) 155:19

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Fig. 2 Population level phylogenetic Tree and Principal component analysis (PCA). (A) The maximum likelihood tree was constructed based onpair-wise FST matrix. And the marked number are bootstrap value; (B) The top two PCs of individuals representing six East Asian populations,mapped to their corresponding geographic locations (generated by R 2.15.2 and Microsoft Excel 2010)

Mongolian individuals are located toward CHB. Interestingly, the distribution of these clusters is generally
consistent with the geographical distribution of these populations (Fig. 2b). We next performed PCA of only Han
Chinese, Japanese and Korean individuals (Additional file
5: Figure S3D), PC1 explained 0.8% of the variance (or 20.
2% of the top 10 PCs) and separated Han Chinese fromJapanese and Korean. PC2 separated CHB and KOR from
CHS and JPT. Japanese and Korean each has its own cluster and both are located apart from Han Chinese while
CHB and CHS cannot be completely separated at thisscale. These results suggested that although similar in appearance, Han Chinese, Japanese and Korean are different

in terms of genetic make-up, and the difference among
the three groups are much larger than that between northern and southern Han Chinese.
K-mean clustering analysis

We classified Han Chinese, Japanese and Korean
individuals by K-means clustering with first 10 PCs(Additional file 6: Figure S4). Assuming three clusters(K = 3), Han Chinese and Korean are not well distinguished, while all the Japanese individuals cluster
perfectly. There are two clusters in Han Chinese andKorean, the north cluster (NC, in orange) and the southcluster (SC, in green). All the Korean individuals belong

Wang et al. Hereditas (2018) 155:19

to NC while CHB and CHS have SC on average of 7.5%
and 67.4% of SC, respectively. Assuming four clusters (K= 4), Han Chinese can be distinguished well from Korean.
The NC at K = 3 separated into Korean cluster (KC,in blue) and north Han Chinese cluster (NHC, inorange) [7]. Around 64.0% of CHB individuals and 9.
0% of KOR individuals are assigned to NHC, while 3.
4 and 32.6% of CHB individuals to KC and SC (ingreen), respectively.
Screen ancestry informative markers

We further explored whether the three populations
could be well distinguished with a small number of ancestry informative markers (AIMs, see Methods). Individuals from any two populations can be distinguishedwith a sufficient number of AIMs, and we use Matthews
correlation coefficient (MCC, see Methods) to measure the clustering ability of AIMs (Additional file 7:
Figure S5). As a result, we could use only 89 SNPs
(CHB/KOR), 46 SNPs (CHB/JPT), 44 SNPs (CHS/KOR),
26 SNPs (CHS/JPT) and 73 SNPs (JPT/KOR) respectively
to perfectly distinguish each population pairs. These AIMscan facilitate discerning and controlling of population
structure among major East Asian populations in futureassociation studies [8].
Origins, divergence time and migration history of Han
Chinese, Japanese and Korean

To reveal the population history of the three East Asianethnic groups, we conducted a series of population genetic
analysis and estimation. We first estimated the effective
population size (Ne) [9] of East Asian and worldwide populations from 5 to 250 thousand years ago (KYA), assuming
25 years per generation [9] (Fig. 3, Additional file 8: FigureS6). Our analysis showed that African population (YRI)
had the greatest Ne before 500 generations ago (GA), and
Ne of European (CEU) was also greater than those of most
East Asian populations (except for CDX and KHV) before
1000 GA. However, East Asian populations all showedstrong recent growth (~ 1% per generation during the recent 500 generations), so that Ne of most East Asian populations exceeded that of YRI during the recent ~ 12.5
KYA. In East Asia, CDX and KHV have the greatest Ne,
followed by CHB. Tibetan (TIB) and Mongolian (BMONand QHM) have greater Ne than CHS and KOR before
500 GA, but grew slower than the later ones in recent
7500 years. Because of low marker density of the data, theestimation of Ne of JPRK might not be accurate, but apparently Ne of JPRK was the smallest. Korean roughly has
the same Ne with Japanese and two Mongolian populations in history, but showed greater increasing rate in recent 12,500 years.
We further estimated divergence time (TF) of each
population pair (Tables 1 and 2) based on FST (Additional

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file 2: Table S2) and Ne following the formula TF = 2NeFST(1) [9]. Most non-African populations diverged fromAfrican ~ 82 KYA, consistent with the time of “Out of
Africa” as suggested by mtDNA studies [9]. European andEast Asian populations diverged later than that between
African and non-African populations. Among East Asian
populations, CDX diverged from CEU ~ 56 KYA, while
the other East Asian populations separated from European
about 35~ 50 KYA.
We found that the present-day Han Chinese andJapanese have the most recent common ancestor thatcan be dated back about 3.0~ 3.6 KYA (corresponding to
the Shang Dynasty in Chinese history). Korean andnorthern Han Chinese had frequent communications inancient time, and the divergence time between the two
populations was estimated as ~ 1.2 KYA (corresponding
to the later period of Three Kingdoms of Korea, or theTang Dynasty in China). And Japanese and Korean
separated ~ 1.4 KYA, a little earlier than that of Han
Chinese and Korean (corresponding to Asuka period inJapan, or in the middle of Three Kingdoms period of
Korea).
Gene flow among east Asian populations

Considering their frequent communication in history,
we are curious about how the initial common origin andrecent gene flow contributed to the genetic make-up of
Han Chinese, Japanese and Korean populations.
STRUCTURE and ADMIXTURE analysis

To examine fine-scale population structure and assess
the genetic make-up of East Asian groups, we applied amodel-based method, STRUCTURE [10], to analyze thegenome-wide data with that of worldwide populations.
East Asian populations showed distinct genetic component inferred from STRUCTURE which is totally different from those of European or African (Additional file 9:
Figure S7). There is no more than 0.5% of African’s
contribution in East Asian’s genome can be detected,
while in Mongolian there is considerable contribution
from European ancestry (12.5~ 16.9%) (Additional file 9:
Figure S7, K = 3). We identified a component which we
named northern East Asian component (NEAC) existing
widely in the East Asian populations (Additional file 9:
Figure S7, in red). On the contrary, southern East Asiancomponent (SEAC) exists mainly in typical mainland populations (CDX, CHB, CHS and KHV) (Additional file 9:
Figure S7, in green), and Ryukyuan component (RC) exists
mainly in island and peninsula populations (JPRK, JPT
and KOR) (Additional file 9: Figure S7, K = 5~ 7, inyellow). QHM has some Tibetan component (TC) (~8.98%) while BMON has very limited TC (~ 0.5%)(Additional file 9: Figure S7, K = 7, in purple). We could
also observe considerable TC contribution in CHB andWang et al. Hereditas (2018) 155:19

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Fig. 3 Effective population size varied along time. Effective population size (Ne) was calculated from LD observations in 2500 distance classes(from 0.001 cM to 2.5 cM with the window size set as 0.001 cM) for each population. Results from 2000 generations ago (GA) to 200 GA are
shown (generated by Microsoft Office 365)

Tibetan but showed considerable contribution fromthe Tibetan component (TC) (3.1~ 14.5%), especiallyin QHM, who migrated to Qinghai-Tibetan Plateau
about 500 years ago and were reported to haveadapted to local environment [6]. TC was also
observed widely in other populations such as Han
Chinese and Korean (2.3~ 12.6%) (Additional file 10:
Figure S8, K = 5, in purple). At K = 6, RC was separated into two different components, one (in pink)
mainly exists in island populations (JPT and JPRK)
and has very little (1.4~ 1.7%) contribution to otherpopulations, and the other (in yellow) exists both inisland populations (JPRK and JPT, 39.1~ 66.7%) and

JPRK (5.2 and 2.6%, respectively) (Additional file 9:
Figure S7, K = 7, in purple).
To further assess finer scale population structure andgenetic composition of East Asian populations, we run
independent STRUCTURE analyses in the ten East
Asian populations without non-Asian population samples(Additional file 10: Figure S8). At K = 3, we observed aclear north/south cline of genetic components. SEAC (ingreen) constituted the majority (92.7%) of the CDX
genome; Mongolian and Tibetan showed distinct composition but both were influenced by NEAC significantly(5.7%~ 15.0%) (Additional file 10: Figure S8, K = 4, in red).
At K = 5, Mongolian had different composition from
Table 1 Pairwise divergence time (TF)
TF(GA)

BMON

CDX

CEU

CHB

CHS

JPRK

JPT

KHV

KOR

QHM

TIB

YRI

BMON

0

603

1399

276

315

321

265

478

238

88

284

3796

CDX

603

0

2256

218

118

482

374

60

320

1527

509

4518

CEU

1399

2256

0

1839

1690

1369

1676

1985

1782

1896

1589

3290

CHB

276

218

1839

0

24

238

122

149

47

1126

199

3948

CHS

315

118

1690

24

0

238

144

73

81

1059

236

3682

JPRK

321

482

1369

238

238

0

79

379

179

894

329

3156

JPT

265

374

1676

122

144

79

0

285

54

1030

279

3685

KHV

478

60

1985

149

73

379

285

0

235

1347

409

4172

KOR

238

320

1782

47

81

179

54

235

0

1050

225

3826

QHM

88

1527

1896

1126

1059

894

1030

1347

1050

0

998

3988

TIB

284

509

1589

199

236

329

279

409

225

998

0

3592

YRI

3796

4518

3290

3948

3682

3156

3685

4172

3826

3988

3592

0

Note: Pairwise divergence time (TF), in units of generations ago (GA), estimated by 2NeFST

Wang et al. Hereditas (2018) 155:19

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Table 2 Results of F4 test
popO

popA

popB

popC

F4(A,O;C,B)

F4(B,O;C,A)

YRI

TIB

CDX

CHB

0.01255

0.01977

YRI

TIB

CDX

CHS

0.010190.02478

YRI

TIB

CDX

KOR

0.01333

0.01780

CEU

TIB

CDX

CHB

0.010630.02007

CEU

TIB

CDX

CHS

0.00957

0.02605

CEU

TIB

CDX

KOR

0.01238

0.01917

YRI

CDX

JPRK

CHB

0.013170.00911

YRI

CDX

JPRK

CHS

0.018180.00843

YRI

CDX

JPRK

JPT

0.00723

0.02230

YRI

CDX

JPRK

KOR

0.011190.01547

CEU

CDX

JPRK

CHB

0.012390.00734

CEU

CDX

JPRK

CHS

0.01838

0.00788

CEU

CDX

JPRK

JPT

0.007280.02052

CEU

CDX

JPRK

KOR

0.01150

0.01442

YRI

TIB

JPRK

JPT

0.00766

0.02798

YRI

TIB

JPRK

KOR

0.012410.02116

CEU

TIB

JPRK

JPT

0.00769

0.02849

CEU

TIB

JPRK

KOR

0.01267

0.02239

Note: population A (popA) and population B (popB) are two donate
populations, population C (popC) is a target population who received geneflow, population O (popO) is an out group. Additional file 14: Figure S10C
shows the model of F4 test. The value of F4(A,O;C,B) stands for the size of thecommon part of the two pathway A to O and C to B (labeled as l) on the tree
weighted by the gene flow proportion α (labeled as αl), and similarly
F4(B,O;C,A) equals (1-α)m. And the calculation of F4 value noted in METHODS

northern populations (BMON, CHB and KOR, 4.3~
41.7%) (Additional file 10: Figure S8, K = 6).
We confirmed the ancestry inference results with another commonly used method, ADMIXTURE [11] (Fig. 4).
We ran ADMIXTURE from K = 2 to K = 12 and chose
results at K = 5 as the best inference according to
ADMIXTURE’s cross-validation procedure (the best K
has the lowest cross-validation error) (Additional file 11:
Figure S9). Similar to the STRUCTURE results, East Asian

individuals demonstrated three typical ancestry components which were entirely different from African orEuropean. No strong influence of African ancestry on East
Asian populations was detected (3.1% in TIB and no morethan 2.4% in any other East Asian populations). However,
we observed considerable presence of European ancestry
in Mongolian population (15.5~ 16.1%) and some other
East Asian populations (up to 6.4%). As shown in Fig. 4,
the three dominant ancestral components in East Asian
populations are NEAC (in red), SEAC (in green) and RC(in yellow). NEAC is also dominant in Tibetan andMongolian and uniformly distributes in Han Chinese(27.3 to 39.4%), Japanese (24.5%) and Korean (39.9%).
SEAC and RC are frequent in CDX/KHV and JPRK
respectively, and both exist in other East Asian populations, while their proportions are generally negatively correlated across populations.
F test and D test

We used the F3 test [12, 13] to detect potential admixture
populations and a significant negative F3 value suggests
existence of gene flow and possible admixture pattern. We
tried each populations combination (F3 (population C;
population A, population B)) and listed those withnegative F3 values (Additional file 12: Table S3). We foundthat to some degree Han Chinese, Japanese, Korean populations each received gene flow from the other two.
According to the F3 results, Han Chinese may havecommon origin with CDX and KHV, and receive geneflow from northern groups (NEAC) and from islands
groups (RC). Japanese has significant JPRK contribution
and can be treated as an admixture of Ryukyuan and other
East Asian populations (Additional file 12: Table S3). The
Korean population also has NEAC, SEAC and RC, but F3
results strongly suggest that it received gene flow directly
through Han Chinese and Japanese (Additional file 12:
Table S3). Our results showed that Mongolian populations(BMON and QHM) were admixed by European and someFig. 4 Admixture analysis of East Asian populations together with world-wide non-Asian populations. Result of K = 5 was shown and each verticalbar represents genome makeup of an sample individual (calculated by ADMIXTURE 1.23 [1] and R 2.15.2)

Wang et al. Hereditas (2018) 155:19

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East Asian populations (for example, Han Chinese or
Japanese, or their common ancestry, Additional file 12:
Table S3), consistent with their known history.
We further performed F4 test [12, 13] to quantitatively
estimate the gene flow from surrounding groups to Han
Chinese, Japanese and Korean populations, respectively.
F4(A, B; C, D) denotes the common part of two drift
route A to B and C to D (Additional file 13: FigureS11C). If A and B are two reference populations, C is atarget population and O is the outgroup, then F4(O, A;
B, C) and F4(O, B; A, C) have linear relation, which isequal to the gene flow ratio multiplied by common drift
route (αl and (1-α)m, Additional file 13: Figure S11C).
Therefore, we can use linear regression method to estimate the gene flow (Additional file 14: Figure S10). As aresult, NEAC contributed 52.2% to CHB, 44.0% to CHS,
19.7% to JPT and 37.5% to KOR; RC contributed 14.3%
to CHB, 12.4% to CHS, 52.6% to JPT and 33.8% to KOR;
SEAC contributed 33.5% to CHB, 43.6% to CHS, 27.7%
to JPT and 28.7% to KOR.
Finally, we applied the D test (4 population test) [12]to qualitatively estimate the gene flow Between Han
Chinese, Japanese and Korean populations (Table 3). In
D test, a significant nonzero D values suggest the existence of gene flow (Additional file 13: Figure S11, more
information can be found in Material and Methodspart). Significant negative values were observed when D
test were applied for comparing gene flow between Han
Chinese and Japanese/Korean, indicating greater geneflow between Han Chinese and Korean than that between Han Chinese and Japanese. We further applied D
test for detecting gene flow between Japanese/Koreanand south/north Han Chinese, the D values were not
Table 3 Results of D test
popW

popA

popY

popZ

D value

Z score

CHB

YRI

JPT

KOR

−0.0055−5.061

CHS

YRI

JPT

KOR

−0.0054−4.865

JPT

YRI

CHB

CHS

0.0014

1.515

KOR

YRI

CHB

CHS

0.0016

1.649

CHB

CEU

JPT

KOR

−0.0062−6.748

CHS

CEU

JPT

KOR

−0.0060−6.596

JPT

CEU

CHB

CHS

−0.0005−0.595

KOR

CEU

CHB

CHS

−0.0004−0.446

Note: Population A (popA) is outgroup, population W (popW) is a reference
population, population Y (popY) and population Z (popZ) are two potential
target populations. If D value is significantly positive, it suggests that the geneflow between population W and population Y was greater than that between
population W and population Z. On the contrary, if D value is significantly
negative, it means the gene flow between population W and population Y
was smaller than that between population W and population Z. If D value is
not significantly positive or negative, it suggests that the gene flow between
population W and population Y are equal to that between population W andpopulation Z, or there is no gene flow between population W and population
Y or between population W and population Z

significantly different from 0, indicating the gene flow between north Han Chinese and Japanese/Korean are almost
equal to that between south Han Chinese and Japanese/
Korean. These results remain consistent when different
outgroups (CEU, YRI) were used in the analysis (Table 3).

Discussion
In this project, we conducted a genome-wide study ofthree East Asian populations based on genome-wide
high-density SNP data. Our results showed that thethree East Asian populations, Han Chinese, Japaneseand Korean, although they resemble each other in appearance, have distinguishing genetic make-up and are
differentiated apparently on genomic level. In thisrespect, we suggest the three East Asian ethnic groups
should be treated as independent populations in the future studies rather than as a single group, particularly inevolutionary studies or medical studies where population
structure matters. In addition to global differentiation,
the AIMs we screened also indicated considerable
differentiation of local genomic regions among the three
populations. For example, several highly differentiated
SNPs (AIMs) are enriched in the CD46 gene (onchromosome 1q32), which is a type I membrane protein
and is a regulatory part of the complement system [14, 15].
These highly differentiated genomic regions could be due
to regional adaptation to some pathogens, although further
studies are necessary to validate these signals and confirm
this conclusion. These results further suggest the importance to consider population difference when medical studies are conducted in the three East Asian ethnic groups.
It is obvious that the genetic difference among the three
East Asian groups initially resulted from population
divergence due to pre-historical or historical migrations.
Subsequently, different geographical locations where thethree populations are located, mainland of China, Korean
Peninsular and Japanese archipelago, respectively, apparently facilitated population differentiation due to physicalisolation and independent genetic drift. Our estimationsof population divergence time among the three groups, 1.
2~ 3.6 KYA, are largely consistent with known history of
the three populations and those related. However, considering that recent admixture could have reduced genetic
difference between populations, it is likely the divergencetime was underestimated.
We detected substantial gene flow among the three
populations and also from the surrounding populations.
For example, based on our analysis with the F3 test,
Korean received gene flow from Han Chinese andJapanese, and gene flow also happened between Han
Chinese and Japanese (Additional file 12: Table S3). These
gene flows are expected to have reduced the genetic
differentiation between the three ethnic groups. On theother hand, we also detected considerable gene flow from

Wang et al. Hereditas (2018) 155:19

surrounding populations to the three populations studied.
For instance, an ancestral population represented by
Ryukyuan have contributed greater to Japanese than to
Han Chinese, while southern ethnic group like Dai havecontributed more to continent populations than to islandand peninsula populations. Contrary to the gene flow
among the three populations, these gene flows from surrounding populations are expected to have increased genetic difference among the three populations if theyoccurred independently and from different source populations. According to our results, the major source of geneflow to the three ethnic groups were substantiallydifferent, for example, the major source of gene flow to
Han Chinese was from southern ethnic groups, the major
source of gene flow to Japanese was from southern
islands, and the major source of gene flow to Korean werefrom both mainland and islands. Therefore, those geneflows might have significantly contributed to further genetic differentiation of the three populations.
The three populations have similar but not identicaldemographical history; they all experience a strong
population expansion in the last 20,000 years. However,according to different geographic distribution, their effective population size and population expansion are different. North Han Chinese has greater Ne than south
Han Chinese in early time. Japanese and Korean have
similar Ne in most of the history, while Korean population expanded much faster than Japanese in recent thousands of years, suggested a higher potential for
population expansion in peninsula than in islands. Ourestimation showed that Han Chinese have much greater
expansion speed than Japanese and Korean, suggested
continental environment has even higher potential for
population expansion than peninsula or islands. However, the estimated Ne increase in East Asian populations
could be, at least partially, explained by recent population admixture which is expected to increase population
genetic diversity thus increase Ne.
Finally, as we briefly mentioned above, natural selection
might have substantially contributed to the genetic
diversity of the populations. We identified several genomic
regions with significant differentiation between Han
Chinese, Japanese and Korean. For example, most highly
differentiated SNPs enriched in the genes that related toneurological system process and cell-cell signal transmission. There are also significant selective signals in detected
in EGF-like domain and Collagen-like domain. The
physiological significance and evolutionary importance of
these selective signals needed more study.

Conclusion
In summary, the genetic structures of the present-day
Han Chinese, Japanese and Korean people were shaped
jointly by initial population divergence, geographicalPage 9 of 12

isolation, subsequent gene flow and possibly regional
natural selection. Our analysis of the genome-wide dataof these populations and their surrounding neighbors
should advance our understanding of population history
and mechanism of human genetic diversity in East Asia.

Methods
Population and samples

Peripheral blood samples of 100 Koreans unrelated
individuals were collected from South Korea. Eachindividual was the offspring of a non-consanguineous
marriage of members of the same nationality within
three generations. Informed consents were acquired
from the participants. All procedures followed were inaccordance with the ethical standards of the Responsible
Committee on Human Experimentation (approved by
the Biomedical Research Ethics Committee of the
Shanghai Institutes for Biological Sciences, No. ER-SIBS261408) and the Helsinki Declaration of 1975, as revisedin 2000. In addition, genome-wide single nucleotide
polymorphisms (SNP) data of 182 unrelated Han
Chinese, and 90 Japanese individuals were collectedfrom the HapMap Project (International HapMap, 2003)
and the 1000 Genomes Project [4], and comparisonswere made with 663 individuals (630 individuals throughQuality Control) representing 8 populations worldwide
from the HapMap Project, the 1000 Genomes Project
and the PanAsia SNP Project [5] (Fig. 1a, Additional
file 3: Table S1).
Genotyping and data quality control

A set of 934,968 loci were genotyped in 100 Koreansamples with Affymetrix Genome-wide human SNP
Array 6.0. *.CEL files containing raw intensity data
were analyzed with Birdsuite version 1.5.3 [16]. Only
autosomal SNPs with missing rate less than 0.05 were
employed for the downstream analyses. For the purposes of this study, only SNPs with Reference Sequence (RS) numbers and vendor-specified strands
were used in combining data. For different purposesof analysis, several combined data sets were generated. The first two data sets (dataset 1 and dataset 2)
contains 13,576 SNPs for 1002 individuals from all
the twelve populations and 733,113 SNPs for 439
individuals from six East Asia populations (BMON,
CHB, CHD, JPT, KOR and QHM). These datasets
were used to estimate ASD. And dataset 1 was also
prepared for principal component analysis [17],STRUCTURE [10] and Admixture [11] analyses, andestimation of gene flow, so that SNPs in high linkage
disequilibrium (LD) have been removed from thisdata set. The third dataset (dataset 3) contains 44,549
SNPs for 596 individuals representing nine East Asian
populations (except JPRK samples), which was used

Wang et al. Hereditas (2018) 155:19

for population structure analyses of East Asian populations only. In the fourth dataset (dataset 4), we kept
all the SNPs genotyped in all the samples respectively,
and used for estimating FST and screening AIMs [8].
The last dataset (dataset 5) was based on the dataset
4 but phased particularly for calculating LD.
To control for potential batch effect during ourdata integration, we corrected allele frequency of
BMON and QHM [6] by comparing genotypes of theidentical CHB and JPT individuals between the data
set of [6] and our own data. Because CHB and JPT
were also included in the same data set together with
BMON and QHM in [6]. To avoid any possible systematic bias introduced during data integration, we
did SNP re-calling for HapMap individuals from raw
Affymetrix intensity data (.CEL files).
Population structure analysis

Genetic difference between populations was measured
by FST which was calculated following an unbiased
estimate [18]. Principal component analysis (PCA)
was performed at individual level using EIGENSOFT[17]. The map was drawn by R (version 2.15.2) based on
the information provided by http://www.mapsofworld.
com/. We used an allele sharing distance (ASD) as ameasure of genetic distance between individuals, anda 1002 × 1002 and a 439 × 439 inter-individual genetic
distance matrixes were generated according to genotypes of 13,576 and 733,113 autosomal SNPs
respectively (dataset 1&2). The tree of individuals wasreconstructed based on ASD distance and using
Neighbor-Joining algorithm [19] with the Molecular
Evolutionary Genetics Analysis software package(MEGA version 4.0) [20]. Trees of populations as wellas components were reconstructed using maximum
likelihood method [21] with CONTML program in
PHYLIP package (version 3.695) [22]. K-mean clustering analysis was done with R software. Ancestry ofeach person was inferred using a Bayesian cluster
analysis as implemented in the STRUCTURE program[23, 10]. We ran STRUCTURE from K = 2 to K = 7
and repeated 5 times for each single K. All STRUCTURE runs used 15,000 iterations after a burn-in oflength 15,000, with the admixture model and assuming that allele frequencies were correlated [23]. Admixture [11] was also employed for the same
purpose.
We screened Ancestry Informative Markers (AIMs)[8] to distinguish Han Chinese, Japanese and Korean.
These AIMs were selected by the following threecriteria: 1) allele frequency highly differentiatedbetween each two populations (measured by FST); 2)
inter-marker distance larger than 500 Kb; 3)
Matthews correlation coefficient (MCC) of a panel ofPage 10 of 12

AIMs based on PC1 not less than 1.0. MCC is
defined as the follows:TP  TN−FP  FN
MCC ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðTP þ FP ÞðTP þ FN ÞðTN þ FP ÞðTN þ FN Þ

ð5Þwhere TP is the number of true positives, TN is thenumber of true negatives, FP is the number of false positives and FN is the number of false negatives [8]).
Estimation of effective population size and divergence time

We first phased the genotype data with BEAGLE[24]. In order to improve the accuracy of the haplotype inference (phasing), we combined those datasetsfrom the same source and did further estimation
independently.
Then we calculated pairwise linkage disequilibrium(r2) of each SNPs pair with genetic distance less than 0.
25 cM in each population. And effective population size
(Ne) of t generations ago (t = 1/2c (2) [9]) can be
estimated for each population in each distance category
as Ne = 1/(4c)*[(1/rLD2)-2] (3) [9], where c is the genetic
distance. All rLD2 are adjust as (rLD2–1/n), where n is
sample size (number of chromosomes) [9]. Divergencetime (TF) can be estimated by 2NeFST (4) [9]. Here, FST
values were still calculated following an unbiased
estimate. Ne was calculated as the average of theharmonic means of the relevant recombination distancecategories. We used distances categories 0.01 cM to 0.
25 cM (corresponding to 200 to 5000 generations ago)to estimate inter-population Ne [9].
Gene flow estimation

We used D test and F test to detect and estimate geneflow [12]. All the variations and Z scores are calculated
by delete-m jackknife [25].
In D test, D value indicates the difference of the twoallele patterns ABBA and BABA (Additional file 13:
Figure S11A). A significant positive or negative D value(|Z| > 2.58, p < 0.01) suggests the existence of gene flow
either from W to Y or from W to Z. X is out group. In
population genetics, D is calculated by^^
Num
i =Deni

ð6Þ

where
^
Num
i ¼ ðw−xÞðy−zÞð7Þ

^ i ¼ ðw þ x−2wxÞðy þ z−2yzÞ
Den

ð8Þ

w, y, x and z are sample allele frequency of population
W, Y, X, Z.
In F test, F2 value is calculated by

Wang et al. Hereditas (2018) 155:19

F 2 ðA; BÞ ¼ E



0

0

a −b

Page 11 of 12

2 

ð9Þ

(a and b are sample allele frequency of population A and B)
and can be looked as the drift on edge A to B in aphylogeny tree. Andh 0 0  0 0 i
F 3 ðC : A; BÞ ¼ E c −a c −b
ð10Þ
denotes the drift on the common path of edge C to A
and C to B (Dx, Additional file 13: Figure S11B). If there
is no admixture, F3 value should be positive. A significant negative F3 value suggests the existence of geneflow [26, 12, 13]. Similarly,h 0 0   0 0  i
F 4 ðA; B : C; DÞ ¼ E a −b c −d
ð11Þ
If there was gene flow in history, the drift from one
population to another would go through more than one
path in the phylogeny tree; the ratio of drift along eachpath is decided by the ratio of gene flow. If there weregene flow from population A to population C (with aratio α) and from population B to population C (with aratio 1-α), thenF 4 ðA; O; C; BÞ ¼ αl

ð12ÞF 4 ðB; O; C; AÞ ¼ ð1−αÞmð13ÞWhere l and m are unknown, but if there is a series of
population C that are admixed with ancestry from both
population A and population B (A’ and B′) with different
ratio, we can use a linear regression to eliminate theseparameters, so that we can calculation each gene flow
ratio α [13].

Additional files
Additional file 1: Figure S1. A heat map of pair-wise FST. A warmer(red) color means a larger FST value (generated by R 2.15.2). (PDF 6 kb)
Additional file 2: Table S2. Global FST values between East Asian
populations and world-wide groups. (DOCX 16 kb)
Additional file 3: Table S1. Genotype data used in this research.
(DOCX 21 kb)
Additional file 4: Figure S2. Individual level Neighbor-Joining Tree. (A)The NJ tree was constructed according pair-wise genotyping difference;(B) Individual NJ Tree of Han Chinese, Japanese, Korean and Mongolian
individuals (generated by MEGA version 4.0). (PDF 6971 kb)
Additional file 5: Figure S3. Principle component analysis (PCA). (A~B)
PCA results of East Asian groups with CEU and YRI. (C) PCA result ofgroups within East Asia populations, excluding JPRK individuals for ahigher marker density. (D) PCA result of four East Asian populations,
including Han Chinese, Japanese and Koreans samples. (PDF 2458 kb)
Additional file 6: Figure S4. K-means Cluster of PCA results. The resultsof Figure S3D were used to conduct the K-means cluster. Individuals thatcluster together according to the top 10 PCs would be painted by thesame color. Results of K = 3 and K = 4 are shown (generated by R 2.15.2).
(PDF 116 kb)

Additional file 7: Figure S5. Matthews correlation coefficient (MCC)
values increase when more AIMs are used to distinguish individuals. X
axis is number of SNPs and Y axis is MCC values. (A) CHB-CHS; (B) CHBJPT; (C) CHB-KOR; (D) CHS-JPT; (E) CHS-KOR; (F) JPT-KOR (generated byMicrosoft Excel 2010). (PDF 99 kb)
Additional file 8: Figure S6. Effective population size varied along time.
Results from 10,000 generations ago (GA) to 200 GA are shown(generated by Microsoft Office 2010). (PDF 112 kb)
Additional file 9: Figure S7. STRUCTURE analysis of East Asian samples
with worldwide populations. Results from K = 3 to K = 7 are shown. Eachvertical bar represents an individual and each color stands for a genetic
component (generated by R 2.15.2). (PDF 141 kb)
Additional file 10: Figure S8. STRUCTURE Analysis of ten East Asian
populations. Results from K = 2 to K = 6 are shown. Each vertical bar
represents an individual and each color stands for a genetic component(generated by R 2.15.2). (PDF 152 kb)
Additional file 11: Figure S9. Cross-validation (CV) plot for theAdmixture analysis. Result of K = 3 to K = 7 are shown. (PDF 158 kb)
Additional file 12: Table S3. Part of F3 test results. (DOCX 18 kb)
Additional file 13: Figure S11. Models used in gene flow study. (A)Model of D test. X is an out group, we put CEU or YRI in X; W is thecontributor while Y and Z are the receivers. By comparing the two allelicpatterns BABA and ABBA, we can infer the relationship of scale of geneflow between W to Y and W to Z. (PDF 30 kb)
Additional file 14: Figure S10. Results of linear regression in F4 test.
For each group (Outgroup O, donate population A, donate population B,
target population X), X axis is F4(B, O; X, A), Y axis is F4(A, O; X, B). (A)Outgroup is YRI or CEU, donate populations are TIB and CDX, target
population is Han Chinese or KOR; (B) Outgroup is YRI or CEU, donate
populations are CDX and JPRK, target population is Han Chinese or JPT
or KOR; (C) Outgroup is YRI or CEU, donate populations are TIB and JPRK,
target population is JPT or KOR (generated by Microsoft Excel 2010).
(PDF 226 kb)
Abbreviations
AIMs: Ancestry informative markers; ASD: Allele sharing distance;
BMON: Buryat Mongolian; CDX: Chinese Dai in Xishuangbanna;
CEU: Northern Europeans from Utah; CHB: North Han Chinese; CHS: SouthHan Chinese; IBD: Isolation by distance; JPRK: Ryukyuan; JPT: Japanese inTokyo; KHV: Kinh in Ho Chi Minh City, Vietnam; KOR: South Korean;
LD: Linkage disequilibrium; MCC: Matthews correlation coefficient; ML
tree: Maximum likelihood tree; NEAC: Northern East Asian component; NJ
tree: Neighbor-joining tree; PCA: Principal components analysis; QHM: Deedu(DU) Mongolians in Qinghai-Tibetan Plateau; RC: Ryukyuan component;
RS: Reference Sequence; SEAC: Southern East Asian component; TC: Tibetan
component; TIB: Tibetan; YRI: Yoruba in Ibadan, Nigeria
Acknowledgements
The authors thank Drs. Charleston Chiang, Lian Deng, Pengfei Qin, MinxianWang, Jing Li, and Xiong Yang for their helpful discussion and comments.
Funding
These studies were supported by the National Key Research and Development
Program (2016YFC0906403), the National Natural Science Foundation of China(NSFC) grant (91731303, 31771388, and 31711530221), the National Science
Fund for Distinguished Young Scholars (31525014), the Strategic Priority
Research Program (XDB13040100) and Key Research Program of Frontier
Sciences (QYZDJ-SSW-SYS009) of the Chinese Academy of Sciences (CAS), and
the Program of Shanghai Academic Research Leader (16XD1404700). S.X. isMax-Planck Independent Research Group Leader and member of CAS YouthInnovation Promotion Association. S.X. also gratefully acknowledges the supportof the National Program for Top-notch Young Innovative Talents of the “Wanren
Jihua” Project. The funders had no role in study design, data collection andanalysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The following additional data are available with the online version of thispaper. Supplementary Tables S1-S3 and Figures S1-S11 are available online.

Wang et al. Hereditas (2018) 155:19

Authors’ contributions
SX conceived and designed the study. YJ contributed partly to sample
collection and genotype data. SX contributed reagents and materials. YWcollected data and performed the analyses, with contribution from DL. SX
and YW interpreted the data and wrote the paper. All authors have read andapproved the manuscript. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.
Ethics approval and consent to participate
All samples were collected with informed consent and approved by the
Biomedical Research Ethics Committee of Shanghai Institutes for Biological
Sciences. Prior to genotyping and analysis, all samples were stripped of
personal identifiers (if any existed). All procedures were in accordance withthe ethical standards of the Responsible Committee on Human
Experimentation (approved by the Biomedical Research Ethics Committee of
Shanghai Institutes for Biological Sciences, No. ER-SIBS-261408) and theHelsinki Declaration of 1975, as revised in 2000.
Consent for publication
Not applicable.
Competing interests
All authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Chinese Academy of Sciences (CAS) Key Laboratory of Computational
Biology, Max Planck Independent Research Group on Population Genomics,
CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai
Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai
200031, China. 2University of Chinese Academy of Sciences, Beijing 100049,
China. 3Integrated Research Center for Genome Polymorphism, Departmentof Microbiology, The Catholic University Medical College, Seoul, Socho-gu
137-701, South Korea. 4School of Life Science and Technology ShanghaiTech
University, Shanghai 201210, China. 5Center for Excellence in Animal
Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223,
China. 6Collaborative Innovation Center of Genetics and Development,
Shanghai 200438, China.

Page 12 of 12

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Received: 19 January 2018 Accepted: 23 March 2018/
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