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    • The Central Asian Landscape: Possible Inquiries into the Population History andStructure of Mongolia through Quantitative Genetic AnalysesR.W. SchmidtINTRODUCTION Mongolia, located in central Asia (see figure 1), has generated variable andextensive genetic analyses, including the possible founding populations of North America(Kolman et al., 1996; Merriweather et al., 1996), modern ethnogenetic hypotheses forgroups currently inhabiting the country and surrounding areas (Nasidze et al., 2005;Keyser-Tracqui et al., 2006; Fu et al., 2007), the likely Y-chromosomal lineage ofGenghis Khan and his male-line descendents and the extensive geographic expansion inwhich it is found (Zerjal et al., 2003), and lastly, the complex processes of unraveling theunderlying genetic variation seen in the larger regional context of central Asia (Comas etal., 1998; Yao et al., 2000; Wells et al., 2001; Oota et al., 2002; Zerjal et al., 2002; Comaset al., 2004; Quintana-Murci et al., 2004; Yao et al., 2004; Bennett and Kaestle 2006;Derenko et al., 2007). The majority of these studies utilize common genetic markers,such as mitochondrial DNA (mtDNA) and Y-chromosome, which have yieldedsignificant findings in anthropological genetic research (for a review see Crawford,2007). This paper will make use of existing research on the genetics of Mongolia andcentral Asia to explore population history and structure of a region that has beeninhabited by a diverse mixture of individuals and groups, whom have occupied favorableand unfavorable environments, and who now define clearly demarcated boundaries in theform of nation-states. Central Asia is a vast territory located at the confluence ofhistorical empires and trade, crossed by the famous Silk Road with contacts to the south 1
    • in India and open to the steppes of the north. This region is essential to understandingcomplex cultural phenomena such as acculturation, assimilation, languages, overlappingeconomies, and ways of life that include migrations, expansions and conquests. These topics will be investigated through current research of genetic markers(including ancient DNA), migration studies in Mongolia and central Asian populations(Perez-Lezaun et al., 1999). Also, biological variation will be investigated through theuse of quantitative trait variation, which may or may not correlate with historical andgenetic findings. Few studies in Mongolian population history and structure have givenprimacy to quantitative analysis. This paper will utilize quantitative trait variation in theform of craniometric measurements as a tool to potentially understand the complexhistory of Mongolia and other nomadic groups now inhabiting the central Asianlandscape. FIGURE 1. Map of Mongolia 2
    • MATERIALS AND METHODS For comparative purposes in evaluating quantitative craniometric data, groupswere aggregated into major geographic regions with some partitioning: China, Japan,Mongolia, Siberia, Southeast Asia, Europe, India, West Africa, North Africa, Mideast(includes Israel, Iran, and Iraq), Russia, and North America (see Table 1). Groupdifferences were calculated by Wilks’ Lambda and discriminant function classification,with significant differences between all groups (p ≤ .001). The Mongolian groups wereaggregated because of small sample sizes. Groups were further combined by time period:Bronze Age, Mongolian period, Hunnu and modern. In addition, one group was labeled“test”. A discriminant function analysis was conducted to ascertain possible groupdifferences (n = 14) that may skew statistical interpretation. The Mongolian “Iron Age”and Mongolian “Bronze Age” did show significant statistical differences (p < .05) andwere therefore excluded from additional analysis (see Figure 2 and Table 2). TABLE 1. Samples used in current study Sample N China 105 Japan 144 North China 54 Mongolia 109 Siberia 10 Southeast Asia 69 Europe 90 India 39 West Africa 36 North Africa 45 Middle East 40 Russia 59 North America 76 876 3
    • FIGURE 2. Mongolian Classification and Group Differences Canonical Discriminant Functions site S China 4 N China Mong Iron Age? Mong Hunnu Mong Period 2 Mong Bronze Mong Modern Mong Modern Function 2 N China Mong Hunnu Mong "Test" Mong Period Group Centroid 0 Mong Iron Age? Mong "Test" Mong Bronze S China -2 -4 -6 -4 -2 0 2 4 Function 1 TABLE 2. R matrix values for Chinese and Mongolian Samples Population S China N China Iron? Hunnu Mongperiod Bronze Modern "Test" S China 0.0000 N China 0.1028 0.0000 M ongolia Iron? 0.0854 0.0936 0.0000 M ongolia Hunnu -0.0672 -0.0664 -0.0769 0.0000 M ongolian period -0.0609 -0.0584 -0.0583 0.0357 0.0000 M ongolia Bronze -0.0401 -0.0669 -0.0673 0.0300 0.0144 0.0000 M ongolia M odern -0.0859 -0.0820 -0.0707 0.0519 0.0372 0.0045 0.0000 M ongolia "Test" -0.0572 -0.0720 -0.0579 0.0347 0.0296 0.0331 0.0458 0.0000 All samples were taken from the University of Michigan’s Museum ofAnthropology database kindly provided by Dr. Noriko Seguchi. Only males were used inthe analysis to facilitate statistical competence. Seventeen craniofacial measurementswere taken on all samples, with no missing data. See Table 3 for traits used in thisanalysis. For definitions of measurements, see Brace and Tracer (1992). Metric variables 4
    • record inherited differences in cranial and facial form and further, configurations in facialform remain stable over considerable periods of time, making them excellent indicatorsof groups similarities and differences (Brace et al., 2001). TABLE 3. Traits used in this analysis with corresponding abbreviations Quantitative Trait Abbreviation Nasal Height nasoht Nasal bone height nasbnht Nasion prosthion length naprlng Nasion basion nasbas Basion prosthion baspros Superior nasal bone width supnasbn Inferior nasal bone width infnasbn Nasal breadth nasbrdt Frontoorbital width subtense at nasion fowsubna Mid orbital width subtense at rhinion mowsubri Bizygomatic breadth bizygoma Glabella opisthocranion glabopis Maximum cranial breadth maxbredt Basion bregma basibreg Basion rhinion basirhin Width at 13 (fronto malar temporalis) fmtfmt Mid orbital width (width at 14) mowidth An analytical model has been used for this study. Quantitative variation will beexplored through the used of an R matrix analysis. R matrix analysis has become astandard method for investigating population structure and history in both modern andprehistoric contexts using quantitative traits due in large part to the interpretive quality ofthe results (e.g. Relethford and Blangero, 1990; Relethford et al., 1997; Steadman, 2001;Stojanowski, 2005). The R matrix (Relethford-Blangero) analysis has a number ofinterpretive qualities that are useful for microevolutionary studies. Genetic distancesbetween pairs of populations can be estimated directly from the R matrix (Harpending 5
    • and Jenkins, 1973; Williams-Blangero and Blangero, 1989) as well as estimates ofphenotypic Fst. Genetic distances represent morphological similarity and differencebetween samples, and serves as an indication of the rate of migration and mate exchange,assuming the effects of random genetic drift are minimal (Relethford, 1996). Fst is ameasure of regional estimates of microdifferentiation (heterogeneity) based on thecontemporary array of allele frequencies (or quantitative traits). Large estimates of Fst arethe result of less gene flow or smaller population sizes, and smaller estimates of Fst arethe result of extensive gene flow between subpopulations. Significance tests for Fst arecalculated from standard errors, following Relethford et al. (1997). The R matrix also another important interpretive function that is used to generateestimates of differential extralocal gene flow by comparing observed and expected levelsof within-sample variability (Relthford and Blangero, 1990). The residual value (thedifference between the observed and expected values) indicate the rate of external allelesbeing introduced into a subpopulation from outside the mating network. Positiveresiduals indicate greater than average external gene flow, and negative individualsindicate the opposite (Reddy 2001). Taken together, these analyses provide a robustinterpretation concerning the details on patterns of group affinity and phenotypicvariation among the selected populations. Raw data sets were analyzed using the quantitative genetics software RMET 5.0,provided by John Relethford (Relethford et al., 1997). RMET allows for trait heritabilityto be estimated. A heritability of 1.0 produced both minimum genetic distances andestimates of minimum Fst that are comparable to other phenotypic studies (Hemphill, 6
    • 1998; Steadman, 2001); however, because a heritability of one for craniometric variation(which includes environmental variance) is not possible, an estimate of 0.55 was usedaccording to Relethford and Blangero (1990). They found that using an average of 0.55for craniometric trait heritability did not significantly alter the results. That is, the averageheritability is a fairly robust one (although see Carson, 2006). This study has used aheritability of 1.0 and 0.55 for comparisons. All tables shown use minimum Fst andgenetic distances (h2 = 1.0). Unless otherwise noted, the results using differential traitheritability were similar.RESULTS Means and standard deviations for the Mongolian sample are shown in Table 4.The results from the R matrix analyses are shown in tables 5 through 7. Table 5 givesdistance to the centroid (rii) and unbiased Fst values for all 13 populations. Table 6displays the results of the Relethford-Blangero residuals and Table 7 gives the results forthe genetic (d2) distances among all sampled populations.TABLE 4. Means and standard deviations for 17 craniometric measurements for the Mongoliansample Trait Mean SD nasal height 53.84 3.44 nasal bone height 27.42 3.12 nasion prosthion length 74.78 5.25 nasion basion 100.76 4.58 basion prosthion 98.08 5.5 superior nasal bone width 11.20 2.35 inferior nasal bone width 19.0 12.50 nasal breadth 26.66 2.24 frontoorbital width subtense at nasion 18.85 3.14 mid orbital width subtense at rhinion 17.71 3.99 bizygomatic breadth 139.94 6.63 glabella opisthocranion 183.81 6.77 maximum cranial breadth 147.84 6.78 basion bregma 130.76 5.35 basion rhinion 103.31 5.60 width at 13 (fronto malar temporalis) 107.58 4.45 mid orbital width (width at 14) 57.32 4.93 7
    • TABLE 5. R matrix results: Genetic distance (biased and unbiased) to the centroid for all 13populations(h2 = 1.0) Population Biased r(ii) Unbiased r(ii) se Chinese 0.079523 0.074761 0.008804 Japanese 0.068142 0.064670 0.006959 North China 0.128733 0.119473 0.015621 Mongolia 0.147474 0.143851 0.010458 Siberia 0.228754 0.178754 0.048388 SE Asia 0.102555 0.095308 0.012334 Europe 0.082653 0.077098 0.009695 India 0.183652 0.170832 0.021954 West Africa 0.283655 0.269766 0.028398 Mideast 0.083717 0.071217 0.014636 North Africa 0.088384 0.077273 0.014179 Russia 0.111038 0.102563 0.013897 North America 0.101747 0.095168 0.011706 Fst = 0.13002 Unbiased Fst = 0.118518 se = 0.004779 TABLE 6. R matrix results: Relethford-Blangero residuals (h2 = 1.0) Within- group Phenotypic Variance Population r(ii) Observed Expected Residual Chinese 0.074761 0.694 0.788 -0.094 Japanese 0.06467 0.688 0.796 -0.108 North China 0.119473 0.703 0.75 -0.047 Mongolia 0.143851 1.19 0.729 0.461 Siberia 0.178754 0.784 0.699 0.085 SE Asia 0.095308 0.692 0.77 -0.078 Europe 0.077098 0.809 0.786 0.023 India 0.170832 0.629 0.706 -0.077 West Africa 0.269766 0.663 0.622 0.041 Mideast 0.071217 0.695 0.791 -0.096 North Africa 0.077273 0.764 0.786 -0.021 Russia 0.102563 0.806 0.764 0.042 North America 0.095168 0.639 0.77 -0.131 8
    • TABLE 7. Genetic distances among 13 populations used in analysis (h2 = 1.0)Pop China Japan NChinaMong S iberia S E Asia Europe India WAfrica Mideast NAfrica Russia NAmericaChina 0.000 0.045 0.079 0.038 0.027 0.051 -0.040 -0.052 -0.021 -0.059 -0.064 -0.056 -0.028Japan 0.049 0.000 0.056 -0.002 0.023 0.023 -0.034 -0.043 0.007 -0.035 -0.028 -0.035 -0.045N China 0.036 0.073 0.000 0.011 0.025 0.037 -0.055 -0.068 -0.060 -0.046 -0.038 -0.040 -0.029Mong 0.142 0.213 0.241 0.000 0.066 0.007 0.027 -0.104 -0.065 -0.064 -0.068 -0.025 0.030S iberia 0.199 0.196 0.249 0.190 0.000 -0.041 -0.032 -0.130 -0.024 -0.086 -0.069 -0.051 0.062S E Asia 0.068 0.114 0.141 0.226 0.356 0.000 -0.045 0.006 0.028 -0.035 -0.042 -0.042 -0.047Europe 0.231 0.210 0.307 0.168 0.319 0.263 0.000 0.000 -0.066 0.043 0.037 0.062 0.019India 0.350 0.322 0.426 0.522 0.610 0.255 0.247 0.000 0.079 0.073 0.059 0.016 -0.020WAfrica 0.387 0.320 0.509 0.543 0.497 0.310 0.479 0.283 0.000 0.001 -0.019 -0.091 -0.053Mideast 0.265 0.207 0.284 0.342 0.421 0.236 0.062 0.095 0.339 0.000 0.072 0.059 0.073N Africa 0.279 0.197 0.273 0.357 0.393 0.257 0.080 0.130 0.385 0.004 0.000 0.073 -0.003Russia 0.289 0.238 0.302 0.296 0.383 0.282 0.055 0.242 0.555 0.056 0.034 0.000 0.019NAmerica 0.225 0.250 0.272 0.179 0.150 0.285 0.134 0.305 0.470 0.182 0.179 0.160 0.000Note: Values in the upper diagonal are derived from the R matrix. Values in the lower diagonal arederived from d2 distances. Visual representation for group affinity is given in Figures 3, 4 and 5. Figure 3 isthe genetic distance map (scaled by the square root of their eigenvalues) produced fromthe Relethford-Blangero analysis. The first two principal coordinates account for 64.6%of the variation. Figure 4 plots group centroids on the first two canonical variates andFigure 5 plots group centroids on the first three canonical variates resulting fromdiscriminant function analysis. The first three canonical variates account for 76.8% of thevariation. 9
    • FIGURE 3. Genetic Distance Map  West Africa 0.4000  SE Asia 0.2000  India Japanese PC2   Chi nese  North C hina 0.0000   Mi deast Siberi a  North Africa  Mon golia - 0.2000  North America  Europe  Russia - 0.4000 0.0000 0.4000 PC1 (37.4%)FIGURE 4. Plot of the first two canonical variates resulting from discriminant function for 13 groups, 17 variables  1.727 Mon golia  0.996 Siberia  0.746 North America  0.723 Europe  0.189 Russia Function2  - 0.196 Chinese  - 0.370 N China  - 0.599 Japanese  - 0.640 N Africa  - 0.657 Mideast  - 0.829 SE Asia  - 1.693 I ndia  - 2.112 W Africa - 1.368 - 0.924 - 0.856 0.041 1.357 1.637 1.668 - 1.300 - 0.900 - 0.511 0.774 1.588 1.663 Function1 10
    • FIGURE 5. Plot of the first three canonical variates resulting from discriminant function analysis for 13 groups, 17 variables  W Afri ca  India   Mong olia SE Asia  North Ameri ca   Mideast Europe   Chinese Si beria   N Africa Japanese  Russia  N ChinaDISCUSSION Little is known about the people of Mongolia prior to the rise of Genghis Khan(Keyser-Traqui et al., 2006). Early in Mongolia’s history, there were many war-like tribesinhabiting the region, usually nomadic similar to other peoples of the central Asiansteppe. These nomadic tribes sometimes united with other peoples of the steppe, forminglarge confederations that routinely threatened places like China, Europe, and the MiddleEast. These confederacies rarely lasted; however these conflicts did redistribute peopleand left particular genetic impressions. Central Asia is a vast territory that has been central to the development of humanhistory because of its strategic location. The territory has been a complex assembly of 11
    • peoples, cultures, and habitats. The area has been occupied since Lower Paleolithic times,and there is evidence of Neanderthal skeletal material in Uzbekistan (Comas et al., 2004). The genetic legacy of the Mongols was expanded with the rise of Temujin (c.1162-1227), otherwise known as Genghis Khan (Chinggis Khaan) and later the formationof the Yuan Dynasty (1271-1368) (Mote 1999). By 1206 all tribes had come under therule of Temujin, who firmly began the establishment of the Mongol Empire. GenghisKhan and his immediate successors conquered nearly all of Asia and European Russia, aswell as sending armies as far west as the Middle East, and south into Southeast Asia. Thiswas the largest land empire known in history (Figure 5). FIGURE 6. Map showing the extent of the Mongol Empire circa 1294 Genghis Khan and his male-line descendents left a large genetic imprint acrossthe Old World by ruling large areas of Asia for many generations. Genghis Khan and hisdescendents would often slaughter large segments of the population under their control,which allowed a new genetic signature to thrive (Mote, 1999; Zerjal et al., 2003). Zerjal 12
    • et al., (2003) suggest the Mongol ruler and his male lineage may be responsible for a“star-cluster” Y-chromosomal pattern found throughout a large geographical areaextending from Central Asia to the Pacific. This “star-cluster” formation (closely relatedlineages) is found in 16 populations extending from the Pacific to the Caspian Sea and isfound in high frequencies (~8%), suggesting they do not result from an event specific toany single population (Zerjal et al., 2003). It is possible that a form of social selection isresponsible for the observed pattern. That is, on the basis of social prestige (descendent ofGenghis Khan), a novel form of selection favored various human populations. Central Asia is a major contact point for many diverse peoples. As such, thehistory and development of the Mongolian population was a complex process affected bythe mixture of ethnically diverse groups (Keyser-Traqui et al., 2006). Importantly, little isknown genetically of this region, which has played a crucial role in the history ofhumankind (the Silk Road), where contacts and trade occurred between the steppepeoples of the north and peoples of India in the south. These contacts should haveresulted in the generation of complex cultural phenomena, such as acculturation,assimilation, language acquisition, overlapping economies, all acting upon the geneticmakeup of diverse groups found throughout central Asia. Comas et al., (1998; 2004) found the central Asian genetic landscape to presentfeatures (such as frequencies of certain nucleotides, levels of nucleotide diversity, meanpairwise differences, and genetic distances) intermediate between Europe and easternAsia, possibly suggesting significant gene flow enhanced as a result from the trade routesalong the Silk Road. Further, these researchers point to mtDNA eastern Asian sequencesin central Asia originating in the Mongols and/or Chinese (Comas et al., 1998). Yao et 13
    • al., (2000) examined mtDNA control region segment I and melanocortin 1 receptor(MC1R) gene polymorphisms along the Silk Road region of China. In congruence withComas et al., (1998) in the larger region of central Asia, both the frequencies of theMC1R variant and the mtDNA presented intermediate values between those of Europeand East and Southeast Asia, suggestive of extensive admixture in this area of increasedcontact and interaction. This study makes use of quantitative trait variation and accordingly, the resultsare similar to the genetic analyses described above. Table 5 shows the results from theRelethford-Blangero analysis. Within-group phenotypic variance is greatest in Mongolia(1.190), indicating greater than expected extralocal gene flow (0.461). In fact, Mongoliahas the highest value of positive residuals. This finding would suggest that significantadmixture has been occurring in Mongolia despite the relative nomadic lifestyle of manygroups. Figures 3, 4, and 5 all suggest an intermediate position for Mongolia betweenEuropean and East Asian populations. Interestingly, although contacts have beenpersistent between central Asia and India, there is little indication that gene flow has beenoccurring between Mongolians and people of the Indian subcontinent. India is seen as aconsistent outlier in all three analyses, clustering closer to the Middle East and NorthAfrica. Genetic distances resulting from the quantitative analyses are also informative.The lowest d2 values for Mongolia are China (0.142), North America (0.179), and Europe(0.168). The R matrix values derive similar results for Mongolia, indicating a closergenetic relationship to the Chinese groups, Southeast Asia, North America, Siberia, andEurope. Kolman et al. (1996) suggest that central Asian groups (including Mongolia) 14
    • represent the closest link between the Old World and the New World using mtDNAdiversity. They feel that the narrow geographic corridor of east Central Asia, extendingfrom Mongolia to the Pacific coast may have served as a starting point for the humanmigration that lead to the colonization of the New World. Although this study does notallow for the more nuanced underlying variation that could support this hypothesis, thedata does suggest an affinity for Mongolians and North American Indian groups.CONCLUSION The analyses conducted in the present study indicate the utility of quantitativegenetic variation. Although the R matrix analysis does not get to greater underlyingvariation for the Mongolian population, it does however show a correlation with recentgenetic studies using mtDNA, Y chromosome and ancient DNA analysis. 15