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Am J Hum Genet. Sep 2001; 69(3): 615–628.
Published online Jul 30, 2001. doi:  10.1086/323299
PMCID: PMC1235490

Paternal Population History of East Asia: Sources, Patterns, and Microevolutionary Processes


Asia has served as a focal point for human migration during much of the Late Pleistocene and Holocene. Clarification of East Asia’s role as a source and/or transit point for human dispersals requires that this region’s own settlement history be understood. To this end, we examined variation at 52 polymorphic sites on the nonrecombining portion of the Y chromosome (NRY) in 1,383 unrelated males, representing 25 populations from southern East Asia (SEAS), northern East Asia (NEAS), and central Asia (CAS). The polymorphisms defined 45 global haplogroups, 28 of which were present in these three regions. Although heterozygosity levels were similar in all three regions, the average pairwise difference among haplogroups was noticeably smaller in SEAS. Multidimensional scaling analysis indicated a general separation of SEAS versus NEAS and CAS populations, and analysis of molecular variance produced very different values of ΦST in NEAS and SEAS populations. In spatial autocorrelation analyses, the overall correlogram exhibited a clinal pattern; however, the NEAS populations showed evidence of both isolation by distance and ancient clines, whereas there was no evidence of structure in SEAS populations. Nested cladistic analysis demonstrated that population history events and ongoing demographic processes both contributed to the contrasting patterns of NRY variation in NEAS and SEAS. We conclude that the peopling of East Asia was more complex than earlier models had proposed—that is, a multilayered, multidirectional, and multidisciplinary framework is necessary. For instance, in addition to the previously recognized genetic and dental dispersal signals from SEAS to NEAS populations, CAS has made a significant contribution to the contemporary gene pool of NEAS, and the Sino-Tibetan expansion has left traces of a genetic trail from northern to southern China.


The evolutionary history of human populations has been characterized by range expansions, colonizations, and recurrent gene flow restricted by isolation by distance (IBD) (Gamble 1993; Cavalli-Sforza et al. 1994; Lahr and Foley 1998; Templeton 1998; Fix 1999; Hammer et al. 2001). The relative importance of these migration processes and other evolutionary forces (such as selection and genetic drift) in shaping the patterns of variation in the human genome is not well understood (Przeworksi et al. 2000). Therefore, one of the main challenges facing human evolutionary geneticists is to disentangle the effects of past population history events from ongoing demographic processes. The study of population history in East Asia is particularly relevant to this endeavor. The reconstruction of patterns of migration involving East Asia is of interest because (1) the route(s) of the earliest dispersals of anatomically modern humans out of Africa are not well understood from the archaeological and paleontological records, and (2) this region serves as a point of origin for subsequent migrations to Japan, Siberia, and the Americas.

Two major routes of migration into East Asia have been proposed: one through central Asia (CAS) and one through southern East Asia (SEAS) (Nei and Roychoudhury 1993, Cavalli-Sforza et al. 1994). The hypothesis that both routes played a significant role in the peopling of Asia has been referred to by Ding et al. (2000) as the “pincer” model. Analyses of classical genetic markers, as well as of dermatoglyphic and somatometric data, are consistent in showing a major division between northern East Asian (NEAS) and SEAS populations, with populations from northern and southern China falling in separate major clusters (Chu et al. 1998). In addition, Cavalli-Sforza et al. (1994, p. 232) inferred that the initial genetic differences between populations arriving in East Asia via different migration routes were maintained despite a long subsequent period of gene flow between regions. A competing model based primarily on dental data derives NEAS populations from the south, solely via dispersals originating in Sundaland (Turner 1990).

Three recent high-resolution studies at the DNA level (Chu et al. 1998; Su et al. 1999; Ding et al. 2000) have reached mutually contradictory conclusions concerning the origin(s), structure, and microevolutionary processes reflected in the contemporary East Asian gene pool. Chu et al. (1998) used a set of autosomal microsatellites to reconstruct phylogenies of East Asian populations. On the basis of the presence of a paraphyletic northern group and a nearly monophyletic southern group, they inferred a distinction between northern and southern populations, as well as a southern origin for northern populations. Su et al. (1999) reached similar conclusions on the basis of a survey of 19 biallelic polymorphisms on the nonrecombining portion of the Y chromosome (NRY) in 25 Asian populations. They inferred support for northern and southern regional clusters through a principal-components analysis. Because northern populations were less polymorphic and had a subset of the NRY haplogroups found in southern populations, they suggested a southern origin for all East Asian populations. Ding et al. (2000) reached very different conclusions on the basis of their study of mtDNA sequences and five autosomal microsatellites, as well as a reanalysis of the polyomavirus JC sequence data of Sugimoto et al. (1997) and the NRY data of Su et al. (1999). They found no support for either a major north-south division or a southern origin of northern populations on the basis of principal-component maps for each marker system and concluded that patterns of variation throughout East Asia are best explained by a simple model of IBD.

To summarize the aforementioned disagreements, Chu et al. (1998) and Su et al. (1999) concluded that a southern origin is most likely, whereas Ding et al. (2000) proposed that recent IBD processes have erased earlier genetic signatures. Thus, rather than a southern origin for NEAS populations, Ding et al. (2000) stressed the potential importance of more recent gene flow from CAS populations, via the Silk Road. Chu et al. (1998) and Su et al. (1999) support a north-south genetic division in East Asia, which Ding et al. (2000) contend is more properly restricted to post-Pleistocene cultural phenomena.

A major shortcoming of these three DNA studies is the lack of sampling in CAS to assess the role of these populations in contributing to the genetic composition of East Asia. Another potential bias can result from oversampling of SEAS populations (Chu et al. 1998; Su et al. 1999). For example, there were more than two times as many individuals and populations from SEAS in the NRY study by Su et al. (1999), compared with populations from NEAS. Moreover, the choice of the 19 NRY markers was influenced by previous knowledge that they were polymorphic in East Asian populations (Su et al. 1999). This could lead to an ascertainment bias when assessing the relative diversity of NEAS versus SEAS populations. The present study was designed to include additional NRY polymorphisms ascertained in a global sample, as well as more populations from both NEAS and CAS. We performed phylogenetic, nonmetric multidimensional scaling (MDS), spatial autocorrelation, and nested cladistic analyses (NCA), as well as analysis of molecular variance (AMOVA), to test the hypothesis of a north-south East Asian division and to assess the relative roles of population history and population structure in the shaping of patterns of NRY variation in East Asia.

Subjects and Methods


We analyzed a total of 1,383 unrelated males, representing 25 Asian populations (table 1). To avoid the possible effects of strong genetic drift, we restricted our sample composition to continental Asia (except in the case of Taiwan, where recent immigrants from mainland China were sampled). The 25 populations were divided into three regional groups, primarily on the basis of geography: 11 formed the NEAS group, 10 formed the SEAS group, and 4 formed the CAS group (fig. 1). Table 1 also presents linguistic affiliations (Ruhlen 1992) and the three-letter and numerical population codes used throughout the remainder of the article.

Figure  1
Map of 25 sampling localities, divided into three regional groupings (NEAS, SEAS, and CAS). Numbers indicate population numerical codes, given in table 1.
Table 1
Haplogroup Frequencies and Population Diversities for 25 Asian Populations, Grouped by Geography and Language

Genomic DNAs from 10 populations were collected in the laboratory of R. Du in China. These populations (and the province of origin) were: Yao (Guangxi), Uygurs (Xinjiang), Hui (Ningxia), Manchu (Liaoning), Tujians (Hunan), Tibetans (Xizang), Zhuang (Guangxi), southern Han (Guangdong), northern Han (Shaanxi), and Yizu (Sichuan). The families of the sampled males had lived at their present locations for at least three generations. Several populations analyzed here (Siberian Evenks, Chinese Evenks, Oroqen, Mongolians, Buryats, Koreans, Miao, Vietnamese, Kazakhs, Altai, Uzbeks, Taiwanese Han, and Malaysians) have been described in our previous studies (e.g., Hammer et al. 2001). Buccal samples from the She population were collected by W. Wang and were extracted in Tucson. DNA samples from the final population, the Kirghiz, were provided by R. S. Wells. All sampling protocols were approved by the Human Subjects Committee at the University of Arizona.

Genetic Markers

Many of the >200 biallelic polymorphisms known on the NRY are highly geographically localized (Hammer and Zegura 1996; Underhill et al. 2000; Hammer et al. 2001). Ideally, a large number of randomly selected markers should be used for studies of geographically dispersed populations, and all markers should be genotyped on all samples. In practice, this is difficult because of limitations in time and materials (Underhill et al. 2000). Thus, the choice of markers is an important consideration for minimizing the ascertainment bias introduced in studies of NRY variation. We chose to survey a set of 42 polymorphisms from the literature and from our own mutation-detection experiments, which were performed on a geographically diverse sample of Y chromosomes (Hammer et al. 2001). In addition, we genotyped another 10 biallelic polymorphisms that were known to be polymorphic in Asia, some of which were used in the study by Su et al. (1999). These latter sites included M20, M119, M134, M172, M173, M174, M175, M217, UTY1-1330+18, and UTY1-3678+537 (Shen et al. 2000; Underhill et al. 2000). We screened all samples with 15 biallelic markers that define the branches on a haplogroup tree (Hammer et al. 2001). Subsequent analysis of a sample was restricted to markers on the appropriate branch of the haplogroup tree.

Statistical Analysis

We used Arlequin 2.0 software (Schneider et al. 1998) to perform AMOVA, to estimate ΦST distances, to test the correlation between genetic and geographic distances (Mantel 1967), and to measure haplogroup diversity. Two measures of haplogroup diversity were employed, including Nei’s h (Nei 1987) and the mean number of pairwise differences among haplogroups (p). We performed nonmetric MDS (Kruskal 1964) on the ΦST distances, using the software package NTSYS (Rohlf 1998). MDS is an ordination technique for representing the dissimilarity among objects (e.g., populations) in an n-dimensional graph, such that the interpoint distances in the graph space correspond as well as possible to the observed genetic differences between populations. The goodness of fit between the distances in the graphic configuration and the monotonic function of the original distances is measured by a statistic called “stress,” wherein a value of 0 is a perfect fit and a value of 1 is a total mismatch. Spatial autocorrelation analysis was performed using the autocorrelation index for DNA analysis (AIDA) (Bertorelle and Barbujani 1995). AIDA is a form of spatial autocorrelation analysis that summarizes genetic variation among individuals as a function of their distance in space. Measures of molecular similarity are estimated within arbitrary intervals, and their departure from null expectations is tested by randomization. Much like correlation coefficients, autocorrelation statistics are positive when individuals are genetically similar at a certain distance, are negative when they are dissimilar, and are expected to be zero under the hypothesis of spatial randomness. A haplotype-based analog to Moran’s I, denoted as “II,” was employed as the measure of spatial effects on haplogroup frequencies (Fix 1999). To test genetic differentiation under an IBD model, we analyzed the regression of genetic distance estimates for pairs of populations on geographic distances. In a two-dimensional stepping-stone model (Kimura and Weiss 1964) with IBD, an approximately linear relationship between genetic distances, ΦST/(1-ΦST), and the logarithm of geographic distances is expected (Slatkin and Maddison 1990; Rousset 1997; Dupanloup de Ceuninck et al. 2000). Analyses of genetic variance that are based on Wright's island model and AIDA cannot provide insights into the relative roles of historical events and ongoing processes in generating patterns of genetic variation (Hammer et al. 2001). For this reason, we also used the approach of Templeton et al. (1995) to disentangle the effects of population history (e.g., contiguous range expansion, long-distance colonization, and fragmentation) from population structure (e.g, recurrent gene flow restricted by IBD and long-distance dispersal). Population structure processes operate over short time intervals and tend to establish migration-drift equilibria, whereas population history events are considered to be nonrecurrent phenomena that disrupt equilibria (Hammer et al. 2001). We used GeoDis version 2.0 (GeoDis Home Page) to conduct NCA. This method attempts to clarify, in terms of inferred population history and/or population structure considerations, the causal factors of any statistically significant associations between haplogroups and geography (Templeton et al. 1995).


Geographic Distribution of NRY Haplogroups in Asia

Of the 52 mutational sites surveyed here, 30 were found to be polymorphic in the NEAS, SEAS, and CAS populations. Figure 2 shows the evolutionary relationships among the 45 haplogroups (h1–h45) defined by variation at these 52 polymorphic sites. Of the resulting 45 global NRY haplogroups, 28 were present in NEAS, SEAS, and CAS populations. In contrast to the survey by Su et al. (1999), NRY haplogroups of NEAS were not found to be a subset of those in SEAS. Fourteen haplogroups were shared among the three geographic regions. Seven haplogroups were shared between CAS and NEAS, and five haplogroups were shared between SEAS and NEAS. Two haplogroups (h14 in CAS and h37 in SEAS) were specific to a single region.

Figure  2
Evolutionary tree for 45 NRY haplogroups (h1–h45). The root of the haplogroup tree is indicated by an arrow. The 52 mutational events, of which the first 42 appear in table 1 in the report by Hammer et al. (2001), are shown by cross-hatches. The ...

NRY Haplogroup Diversity

NRY haplogroup diversity for each individual population and for the three major regional groups is also shown in table 1. Nei’s (1987) diversity statistic, h, which is based on the frequency and number of haplogroups, ranges from .170 in the Oroqen to .928 in the Hui, both from northern China. The mean regional h value was the highest in CAS, followed by SEAS and NEAS. As in the case of h, the mean number of pairwise differences among haplogroups, p, ranged from a low value in the Oroqens (1.09) and to a high value in the Hui (4.84). At the regional level, however, p values showed a different pattern: the highest p value was found in NEAS, whereas SEAS had the lowest p value. The relatively low mean number of pairwise differences among haplogroups in SEAS occurred because 85% of SEAS Y chromosomes belong to seven closely related haplogroups (e.g., the haplogroups marked by M175 only differed by an average of 2.75 mutations) (fig. 2). The high values of p in populations from NEAS and CAS reflect a more marked divergence among haplogroups in these regions.

MDS Plot

Figure 3 portrays the results of MDS based on ΦST genetic distances. All SEAS populations cluster together on the left half of plot, whereas nearly all NEAS populations occupy the right half of the plot. The only exceptions are the northern Han Chinese, Korean, and Manchu populations, which have closer genetic affinities with southern populations than with northern populations. All but one of the CAS populations (the Kazakhs), as well as the Uygurs from western China, occupy the lower right part of the plot. The stress value (.10) of the MDS plot indicates a good fit between the two-dimensional graph and the original distance matrix.

Figure  3
MDS plot of 25 Asian populations, based on ΦST genetic distances. For three-letter population codes, see table 1.


Table 2 presents variance components and three Φ statistics at three different grouping levels, which summarize the geographic partitioning of NRY diversity. When all 25 populations were combined into three regions, the overall ΦST was .31 (i.e., ~69% of the variance occurred within populations). This value was surprisingly close to our global ΦST of .36, which is based on 2,858 males from 50 worldwide populations (Hammer et al. 2001). The among–populations within groups variance component and the among-group variance component showed similar values of ~15% each. NRY differentiation was significantly higher among NEAS populations (ΦST=.23) than among SEAS populations (ΦST=.09). CAS populations showed an intermediate value (ΦST=.12), which may partially reflect the fact that only four populations were sampled from this region. To identify the extent of among-group variation, we performed AMOVA for pairwise groupings. The highest resulting between-group value was found between SEAS and CAS (ΦCT=.28), followed by the value between SEAS and NEAS (ΦCT=.16). Consistent with the MDS plot (fig. 3), the among-group variance component between CAS and NEAS was not statistically significant (ΦCT=.04, P=.156).

Table 2
Results of AMOVA[Note]

Spatial Autocorrelation Analysis

AIDA analysis was performed three times, initially including all 25 populations and then separately analyzing the SEAS and NEAS regional groupings. In the analysis of all populations (fig. 4a), the II values are positive and significant for distances <2,400 km and are negative and significant for distances >3,000 km. Although the overall correlogram does not exhibit a monotonic decrease, the pattern is very clearly clinal. The peak at 4,800 km shows that some geographically distant populations are not correspondingly genetically distant. The autocorrelation indices show very different patterns of variation in the NEAS and SEAS groupings (fig. 4b). In NEAS populations, the values of II generally decrease from positive and significant to negative and significant as geographic distance increases. The NEAS populations show some evidence of IBD, because populations within 600 km of one another are very similar. However, there is no strong geographic structuring at greater distances. Significantly negative dips at ~1,200–1,800 km and ~3,000–3,600 km were followed by upward fluctuations. Such a pattern is referred to as “long distance differentiation” in classical spatial autocorrelation studies (Sokal et al. 1989; Barbujani et al. 1994). These patterns are regarded as ancient clines on which the effects of gene flow, genetic drift, and/or adaptation to local environmental factors have been superimposed. There is no evident structure in SEAS populations, aside from the expected increased similarity among populations within the zero distance class. This lack of a structural signal may be due to insufficient sampling in the SEAS region.

Figure  4
Spatial autocorrelation plots. a, 25 Asian populations. b, NEAS (diamonds) versus SEAS (circles) populations.

IBD Model and Mantel Tests

Plotting the ΦST/(1-ΦST) distances versus the log of geographic distances for 25 Asian populations (fig. 5a) indicated a significantly positive relationship between genetic and geographic distances (r=.357; Mantel test: P=.000). This result should be interpreted with caution, because Slatkin (1993) showed that a “signature” of IBD can result from regional differences in patterns of gene flow. Because the AMOVA and AIDA results revealed different patterns of variation in NEAS and SEAS, we plotted genetic versus geographic distances separately for NEAS and SEAS populations (figure 5b and and55c, respectively). Whereas northern groups exhibited a statistically significant positive correlation (r=.349, P=.022), southern groups showed a statistically significant negative relationship between genetic and geographic distances (r=-.381, P=.033).

Figure  5
Regression of genetic-distance estimates for pairs of populations against the log of geographic distances. a, 25 Asian populations. b, NEAS populations. c, SEAS populations.


The nesting methodology of Templeton and Sing (1993) produced 14 one-step clades, 6 two-step clades, 3 three-step clades, 1 four-step clade, and a single five-step clade that nested the entire cladogram (fig. 6). A random permutation procedure indicated highly statistically significant associations between clades and geographic locations for the entire cladogram. Of a total of 25 nested clades, 17 exhibited statistically significant associations with geography. With the aid of a key published on the GeoDis 2.0 Web site, we were able to infer the probable causes of these 17 patterns (table 3). Consistent with our previous analyses (Hammer et al. 1998, 2001; Karafet et al. 1999), the present study indicated that the distribution of NRY haplogroups has been influenced by both population history and population structure factors. In contrast with previous results, a larger proportion of the signals (11 of 17) were the result of population structure processes such as recurrent gene flow restricted by IBD (n=10) and long-distance dispersal (n=1). Only six signals involved unique historical events such as contiguous range expansions (n=4) and long-distance colonizations (n=2).

Figure  6
Nested cladistic design for 45 NRY haplogroups. The 52 mutational events (cross-hatches), 45 haplogroups (h1–h45), and cladogram root (arrow) are as described in figure 2. Black circles represent haplogroups that were missing in this sample of ...
Table 3
Inferences from NCA


Geographic Patterns of Diversity in East Asia: Is There a North-South Division of NRY Variation?

Important differences between NEAS and SEAS populations have been noted in many studies based on both genetic and morphological characters (Cavalli-Sforza et al. 1994). These findings have inspired several hypotheses concerning the origin of the Chinese people (Matsumoto 1988; Zhao and Lee 1989; Chu et al. 1998; Qian et al. 2000), the origin of linguistic families in Asia (Chu et al. 1998; Su et al. 2000), and prehistoric migrations to and within Asia (Nei and Roychoudhury 1993; Cavalli-Sforza et al. 1994; Su et al. 1999, 2000).

The present survey increased both the number of populations examined for markers on the NRY and the number of NRY markers studied in East Asian populations. Populations were sampled from NEAS and SEAS, as well as from CAS. The patterns of NRY haplogroup variation presented here have several features in common with other data sets, as well as several features that are novel. The MDS plot in figure 3 shows that the majority of SEAS populations are clearly differentiated from NEAS populations. Despite the extremely high internal diversity in the SEAS region (within-population variance = ~91%, table 2), the results of both the MDS plot and AMOVA are consistent in demonstrating a closer genetic relationship between CAS and NEAS populations than between either of the former groups and SEAS populations. This fact alone makes it clear that the genetic history of CAS populations must be considered in any analysis of the origins of East Asian populations (Ding et al. 2000).

In contrast to the results reported by Su et al. (1999), our results do not indicate that southern populations are more polymorphic than northern populations in East Asia, or that haplogroups found in the north represent a subset of those in the south (table 1). On the contrary, 26 haplogroups were found in the north, whereas SEAS populations had only 21 haplogroups. Although caution should be exercised when comparing levels of diversity among different studies (Hammer et al. 2001), our results do not support the general conclusion of an exclusively southern origin for NEAS populations.

Two possible reasons for the difference between our results and those of previous studies of NRY variation in Asia include thinner sampling of NEAS populations in the study by Su et al. (1999) and/or different choices of NRY markers. To assess the contribution of these two sources of bias, we compared the ratio of northern to southern diversity in our data set (i.e., p in NEAS versus p in SEAS) with (1) a similar ratio based on the data presented by Su et al. (1999) and (2) a similar ratio based on our data but restricted to the haplogroups analyzed by Su et al. (1999) (using our markers, we were able to construct 13 of the 17 haplogroups analyzed by Su et al. [1999]). In the study by Su et al. (1999), there was a relatively low ratio of NEAS to SEAS diversity (p ratio=0.88). When we restricted the analysis of our data to the haplogroups analyzed by Su et al. (1999), we found a slightly higher ratio of NEAS to SEAS diversity (p ratio=1.2); however, this was not as high as the diversity ratio in our study (p ratio=1.42). These rough comparisons suggest that both the markers used and the populations sampled contributed to differences between the results of Su et al. (1999) and those presented here.

There were also contrasting patterns of NRY haplogroup diversity within the three regions surveyed here. Nei’s (1987) diversity statistic (h) was slightly lower in NEAS than in SEAS. A larger effect of genetic drift may be part of the explanation for this observation because there are lower population densities and smaller population sizes in the north. The AMOVA results are consistent with this explanation. While significant genetic structure was observed in both regions, a nearly threefold-higher ΦST value was found in NEAS compared with SEAS. If the extent of molecular differences between haplogroups is not taken into account, and genetic structure is estimated only from haplogroup frequencies, we still observe a higher ΦST value in the north than in the south (.157 vs. .101, respectively; data not shown), although this is a less marked numerical difference than for the corresponding ΦST values. This result suggests that both genetic drift and haplogroup composition have played important roles in the differentiation of populations in these two regions. Because of the large pairwise differences found in NEAS populations, it appears that highly divergent haplogroups are very important in distinguishing the populations within NEAS. This may reflect migrations from different source populations.

How does the among-group variance component between NEAS and SEAS populations compare with other regions of the world? A direct comparison can be made with the ΦCT value observed between CAS and SEAS populations, which was ~75% higher (table 2). With the caveat that we are comparing across studies using different but overlapping sets of NRY markers, the level of differentiation between NEAS and SEAS populations observed here (i.e., ΦCT=.16) is intermediate with respect to levels of differentiation between Middle Eastern and European populations (ΦCT=.10) and between northern African and European populations (ΦCT=.27) (Hammer et al. 2000).

Factors Shaping Genetic Variation in East Asia: Testing the IBD Model

The MDS, AMOVA, and NCA results provided evidence of geographic structuring in East Asia. We wanted to discern what factors shaped patterns of NRY diversity in East Asia, and, in particular, we wanted to test the hypothesis of Ding et al. (2000) that NRY marker patterns result from simple IBD. The term “isolation by distance” was introduced by Wright (1943) to describe the accumulation of local genetic differences under geographically restricted dispersal. Under the IBD model, genetic differentiation at neutral loci increases with geographic distances (Wright 1943; Malécot 1968; Morton 1973). Although this model is more suitable for short-range migrations between neighboring populations, covariation of geographic and genetic distances has been observed at a large geographic scale in several human population systems (Jorde 1980; Excoffier et al. 1991; Cavalli-Sforza et al. 1992, 1994; Barbujani and Pilastro 1993; Poloni et al. 1997; Hammer et al. 2000).

To distinguish the pattern of increasing genetic distance with geographic distance from the process(es) generating this pattern, we refer to the pattern as “spatial correspondence.” For example, when we considered all 25 populations from the CAS, NEAS, and SEAS regions, there was a statistically significant positive correlation between genetic and geographic distances (fig. 5a). This pattern of spatial correspondence is consistent with IBD (i.e., equilibrium under geographically restricted gene flow); however, this inference must be treated with caution, since the geographic region under investigation is vast, and other processes may be responsible for the observed positive correlation (Barbujani and Sokal 1991). When the NEAS and SEAS populations were analyzed separately, we found very different patterns (fig. 5b and and5c).5c). This leads to the inference that NRY structuring in the SEAS region is not maintained by recurrent gene flow among local populations. The apparent lack of an IBD signal may be due to several nonmutually exclusive factors, including recent population movements, language and/or cultural boundaries, sampling at an inappropriate scale, and/or unrealistic assumptions of the IBD model (Slatkin 1993; Zegura et al. 1995; Rousset 1997; Dupanloup de Ceuninck et al. 2000).

Spatial autocorrelation (Sokal and Oden 1978) is another method for analyzing geographic patterns of genetic diversity. This method compares data within each of several distance classes, and inferences are based on the degree of genetic similarity at various geographic distances. The particular method of spatial autocorrelation employed here, AIDA (Bertorelle and Barbujani 1995), also takes sequence differences among haplogroups into account. In other words, two localities are considered more alike if the same haplogroups occur at similar frequencies and if the various haplogroups differ by fewer mutations. The distribution of II across distance classes (i.e., the correlograms) can be compared with patterns predicted by different evolutionary processes. The resulting patterns are interpreted as clines, depressions (i.e., clinal variation encompassing only a part of the study area), IBD, random genetic variation, or as reflecting various selective regimes (Sokal 1979; Fix 1999; Barbujani 2000).

Spatial patterns of genetic similarity did not show a pure pattern of IBD in our data set (fig. 4). Under IBD, variations in population size affect only the impact of genetic drift (i.e., the larger the population, the smaller the allele-frequency fluctuations). When only drift and short-range dispersal affect populations, genetic similarity is expected to decrease from positive to insignificant in spatial autocorrelation analysis (Barbujani et al. 1994). NRY variation over the whole range of samples is more consistent with a clinal pattern than with an IBD pattern. Clines are usually associated with distinct population movements. Demic diffusion (a combination of demographic growth, range expansion, and limited admixture) is an example of a form of directional population expansion causing allele-frequency clines (Ammerman and Cavalli-Sforza 1984). Clines resulting from demic diffusion can be stable for long periods of time (Cavalli-Sforza et al. 1994). Clines may also be generated by loss of genetic variation through repeated founder effects occurring in a phase of population expansion not accompanied by admixture (Barbujani et al. 1995; Fix 1999), by admixture between two genetically distinct groups initially separated by a nonpopulated area (Endler 1977), or by a selection gradient (Fix 1999).

In our NEAS sample, 7 of 11 populations are speakers of languages within the Altaic linguistic family. Our samples from SEAS include four Sino-Tibetan–speaking populations, five Austro-Asiatic–speaking populations, and one Austronesian-speaking population. Austro-Asiatic and Austronesean languages are considered to be branches of the Austric linguistic superfamily (Ruhlen 1992). In a study based on classical markers, Altaic-speaking populations exhibited a pattern of clinal variation (Barbujani et al. 1994). Their data and our NRY results (i.e., evidence for ancient clines in the NEAS correlogram plot, fig. 4b) are both consistent with Renfrew’s (1991) prediction that Altaic languages should be included among those that were propagated by demic diffusion. This demic-diffusion hypothesis is also concordant with the inference that population history has played a key role in generating patterns of NRY variation in NEAS populations. On the other hand, NRY variation appears generally random even at small distances in SEAS. Likewise, Barbujani et al.'s (1994) classical marker study indicated that populations of Sino-Tibetan speakers and Austric speakers exhibited random genetic variation even at small distances. Under an IBD model, positive autocorrelation at the first distance classes must be observed. The insignificant and even negative autocorrelation detected at relatively short distances for SEAS populations suggests the rather puzzling conclusion that short-range gene flow was not important in establishing the observed levels of diversity among these populations.

The difference that we see between patterns of variation in NEAS and SEAS populations could be random, because an insufficient number of populations was included in our analyses. Another, and perhaps more cogent, explanation points to the different linguistic and cultural affiliations of populations inhabiting NEAS and SEAS. Interestingly, when we performed AMOVA on populations grouped according to a linguistic-family criterion (i.e., Sino-Tibetan, Altaic, Austro-Asiatic, and Austronesian), among-group distances were slightly larger (ΦCT=.18, data not shown) than when populations were grouped into the three major geographic regions (ΦCT=.16, table 2). To summarize, a high level of population subdivision (caused or enhanced by linguistic barriers) may have led to a random distribution of NRY diversity in SEAS. On the contrary, in NEAS there was a significant cline, consistent with the effects of large-scale directional expansions of Altaic-speaking populations. Recurrent gene flow within short geographic distances (suggested by the NCA results) probably affected genetic diversity at a local scale and was reflected in the fluctuations of autocorrelation indices at intermediate distance classes. Nevertheless, the difference between the NEAS and SEAS regional gene pools was large enough to produce a broad gradient when all populations were jointly analyzed by spatial autocorrelation.


In sum, it appears that different kinds of evolutionary forces shaped patterns of genetic variation in East Asia. Although it is difficult to quantify the genetic impact of major population movements in the past, our results seem to be incompatible with random genetic variation and/or pure IBD as the only explanations for the patterns of NRY variation in East Asia. Our current results also suggest that some recent conclusions concerning the origin(s) of East Asian populations may have been premature. We do not find the simple ancestor-descendant relationship between SEAS and NEAS populations suggested by Su et al. (1999). Chu et al. (1998) posited that Altaic-speaking populations occupying the most northern parts of Asia originated from an East Asian population that was originally derived from SEAS. Their reasons were based, in part, on the claim that because the last glaciers started to recede only 15,000 years ago, an early migration route from CAS to Siberia was unlikely. However, the great boreal forest or taiga of Siberia was established in essentially its present character early in the Pleistocene and was never wholly displaced. Central and southern Siberia were not under glaciers and constituted a major refugium in northern Eurasia during the periods of maximum cold (Kuzmin and Orlova 1998).

The very different patterns of variation in NEAS and SEAS may better fit a two-prong (i.e., pincer) model for the origins of East Asian populations, similar to that championed by Cavalli-Sforza et al. (1994). Certainly, the closer genetic and linguistic relationships of CAS and NEAS populations lend support to the hypothesis of a separate migratory route coming from CAS (Cavalli-Sforza et al. 1994). Although we found a rather clear dichotomy of NEAS and SEAS populations, the sharing of haplogroups between southern and northern populations may be explained by subsequent short-range and long-range migration processes, perhaps associated with the advent of agriculture and animal domestication. Although this postulated south-to-north gene flow has begun to obscure the initial genetic differences between NEAS and SEAS, it has certainly not yet erased them (contra Ding et al. 2000). Su et al.’s (2000) hypothesis that the Sino-Tibetan language family originated near the Yellow River (in the NEAS region) and subsequently dispersed to the SEAS region adds an additional complication, this being the strong possibility of bidirectional gene flow across the Yangtze River during the Holocene (fig. 1). This hypothesis of relatively recent bidirectional migration is clearly underscored by visual inspection of figure 2. For instance, most of the haplogroups derived from (i.e., to the left of) the mutational site M175 are present at relatively high frequencies in SEAS populations (yellow) and are shared primarily with NEAS populations (blue) rather than with CAS populations (red). These haplogroups may well have originated in SEAS and spread to the north (Su et al. 1999, 2000). On the other hand, many of the haplogroups derived from the backbone of the gene tree are shared between NEAS and CAS populations and account for the majority of NEAS haplogroups. This phylogeographic pattern may be a signal of dispersals from CAS towards NEAS; moreover, the minor sharing of these haplogroups with SEAS populations could, indeed, be the signal of north-to-south dispersals such as the aforementioned Sino-Tibetan expansion. These observations imply that future, more realistic models for the underlying processes leading to the modern population structure of East Asia will have to accommodate more complex multidirectional biological and—especially—cultural influences than earlier explanatory paradigms.


We thank Ji Park, Svetlana Resnikova, Rupesh Amin, and Amit Indap, for excellent technical assistance. We are especially grateful to Guido Barbujani, for his ongoing interest in, and helpful suggestions for, the development of our ideas. We also thank two anonymous reviewers who made significant improvements in our manuscript. This work was supported by National Science Foundation (NSF) grant OPP-9806759 and National Institute of General Medical Sciences grant GM-53566-06, both to M.F.H. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NSF or NIH. Partial support for the research of W.W. and S.F. came from City University of Hong Kong grant number 9010001.

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