Geographic variation of six dermatoglyphic traits ... - Wiley Online Library

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 90:393407 (1993)

Geographic Variation of Six Dermatoglyphic Traits in Eurasia ROBERT SOKAL AND GREGORY LIVSHITS Department of Ecology and Euolution, State University of New York, Stony Brook, New York I 1 794-5245(R.S.1;Research Unit-Human Population Biology, Division of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Auiv University, Tel Aviv, Israel 69978 (G.L.)

KEY WORDS Asia

Patterns of variation, Spatial variation, Europe,

ABSTRACT We describe the geographic variation patterns of six dermatoglyphic traits from 144 samples in Eurasia. The methods of analysis include computation of interpolated surfaces, one-dimensional and directional correlograms, correlations between all pairs of surfaces, and distances between correlograms. There are at least two, probably three, distinct and significant patterns of variation. 1)A general NW-SE trend for pattern intensity, the main line index, and frequency of hypothenar patterns. 2) A trend from the Middle East to the north and east for frequency of axial triradius and of accessory interdigital triradii. 3) A patchy pattern for frequency of the thenar-interdigital 1. The results are compatible with a diffusion process between Europe and the peoples of Northern Asia, and possibly with a radiation of populations from the Middle East. The hypothesis of diffusion processes is supported by substantial interpopulation correlations between dermatoglyphic traits that contrast sharply with largely negligible intralocality correlations. 0 1993 Wiley-Liss, Inc. Numerous studies have investigated the The aim of the present study is to describe the main geographic trends in six dermato- genetic components of various dermatoglyphic traits in Eurasia and to examine the glyphic traits (e.g., Meier, 1980; Loesch, evidence they furnish on the structure and 1983; Borecki et al., 1985). At present we still lack an overall understanding of the origins of these populations. The earliest population surveys employ- modes of inheritance of dermatoglyphic feaing dermatoglyphics go back to the begin- tures. However, there is no question that the ning of this century (e.g., Wilder, 1904, phenotypic variance of the majority of the 1913; Hosebe, 1918; Keith, 1924). Since that finger and palmar dermal patterns shows time thousands of studies assessing numer- considerable family correlation, i.e., genetic ous and diverse populations have been pub- heritability; in many instances this is higher lished (Mavalwala, 1977).Many of these and than 0.60 (Jantz, 1979; Loesch, 1983). The subsequent studies have revealed extensive six variables considered in the present study differentiation within and between ethnic have been found to have quite high heritagroups and local populations (e.g., Plato et bilities. Estimates of h2 of the hypothenar al., 1975; Pollitzer and Plato, 1979; Heet, patterns on the palm range between 0.63 1983; Chakraborty, 1990; Kamali and and 0.76 (Katayama, 19811, with those for Mavalwala, 1990). The biological relation- main line terminations and thenar patternships among the major human races with ing approximately in the same range respect to dermatoglyphic traits have been (Froelich, 1976; Roberts, 1979). Loesch investigated by Thoma (1974),Rothhammer et al. (1977), Heet and Keita (1979), Kamali Received April 21, 1992; accepted September 16,1992 et al. (1986), and Pons (1990). 0 1993 WILEY-LISS, INC.

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R. SOKAL AND G. LIVSHITS

(1983) noted that most studies agree on a heritability higher than 0.65 for palmar and axial triradii. The pattern intensity index has been found to be the dermatoglyphic trait with the highest level of genetic determination, h2 > 0.80 (Froelich; 1976). The wide availability of dermatoglyphic data and the high heritabilities of dermatoglyphic measures make these variables well suited for the study of human variation. However, few large-scale studies of geographic variation of dermatoglyphic variables have been conducted (Schwidetsky, 1962; Heet, 1983; Heet and Dolinova 1990). Furthermore, there are no studies that use quantitative analyses of patterns and trends. Substantial methodological progress has been made in recent years in the analysis of spatial patterns of human blood markers (Sokal and Oden, 1978a,b; Sokal and Wartenberg, 1981; Piazza et al., 1981). The application of these techniques to European gene frequencies revealed the main geographic distribution patterns of various alleles in Europe, and also the primary evolutionary forces that formed the modern genefrequency surfaces of the continent (Sokal et al., 1989). This methodology, in particular spatial autocorrelation analysis, has been applied recently to geographic variation patterns of quantitative craniometric and anthropometric variables (Sokal and Uytterschaut, 1987; Sokal and Winkler, 1987), as well as to a few dermatoglyphic variables at the smaller geographic scales (Sokal and Friedlaender, 1982; Dow et al., 1987). In particular, close association within the Solomon Islands was found between language dissimilarity and dermatoglyphics, while controlling for geography (Dow et al., 1987). These authors also noted that dermatoglyphics were even more useful than anthropometric, odontometric, or blood markers in assessing the historical relationships between populations in this area. However, no such studies have been carried out on a larger geographic scale, as in this paper. MATERIALS AND METHODS Traits

Although very many dermatoglyphic studies have been published, their results

are not often comparable. The main reasons are first, that dermatoglyphic traits were not always defined objectively (Meier, 1980), and second, that different authors used different sets of variables in their studies. Therefore, the present study was restricted to the six most commonly used traits; information was available for 144 populations located on the Eurasian supercontinent. The following six dermatoglyphic traits were studied in males: 1)pattern intensity index (PII); 2) main line index (MLI); 3) frequency of the axial triradius (T); 4) frequency of the hypothenar patterns (HY); 5) frequency of the accessory interdigital triradii (AIT); and 6) frequency of the thenarinterdigital 1pattern (TW1). Most dermatoglyphic traits are strongly intercorrelated (Chopra, 1979; Loesch, 1983) and, therefore, their study might be redundant. However, the within-population correlations among the aforementioned six traits are low (r < 0.3) and statistically not significant (Heet, 1983). Readers will note that we have omitted finger ridge counts from our analyses. These would have been very desirable because of their high heritabilities, but we lacked a sufficient number of samples for these variables and the available samples were frequently not from the same locations as those of the variables studied. Sources of data Most of the data were obtained from Heet (1983) and Heet and Dolinova (1990). These authors have gathered data for the above six variables from various populations of the former Soviet Union and other European and Asian countries. Records for several other ethnic groups were extracted from Plato et al. (19751, Castilla (19791, Katayama (1982), Gualdi-Russo et al. (19821, Gualdi-Russo (19871, and Luna and Pons (1987). The data comprise about 120 different ethnic groups, belonging to 27 language families that fall into 12 language phyla. On morphological grounds, the samples are representatives of or intermediates between two major human races, centered on Europe and northern and eastern Asia (called Europids and Mongolids by Schwidetzky,

DERMATOGLYPHIC TRAITS

1974). Designations of the ethnic groups, their geographic coordinates, descriptive geographic information, and the source of the data are furnished in Appendix A. The localities are shown mapped in Figure 1. When the information on the exact location of a sample was available in the source, the coordinates were specified from the Rand McNally New International Atlas (Rand McNally, 1980). Otherwise, coordinates for the national capital of the country, or the main city of the area were used. For example, since only a single Polish sample was available, without specific reference to a sample location, we adopted the coordinates of Warsaw for this sample.

395

spatial autocorrelation) to - 1 (negative autocorrelation), the expected value being - 1/ ( n - 11, where n is the number of localities. Geographic distances between localities were computed as great circle distances. Autocorrelation coefficients were computed within 10 arbitrary distance classes, whose upper limits were 250, 500, 1,000, 1,500, 2,000, 3,000,4,000,6,000,8,000, and 13,036 km. The plot of the autocorrelation coefficients vs. distances is referred to as a spatial correlogram, the overall significance of which is assessed through a Bonferroni test (Oden, 1984). The directions of the spatial trends were investigated by means of directional spatial correlograms (Oden and Sokal, 1986). In Methods of analysis this case, the autocorrelation coefficients To obtain a visual representation of der- (Moran’sI and Geary’s c ) were computed in matoglyphic variation in geographic space, classes based not only on the distance bewe approximated the surfaces of the six tween pairs of localities, but also on the muvariables by interpolating to obtain a quasi- tual compass bearing of each pair of points. continuous set of variates from discrete data Seven distance classes were chosen, reprepoints. To this end we employed the sented as annuli in the correlogram plots. SURFER program (Golden Software, Inc.). The upper limits of the classes were 500, The paths of the contour lines were calcu- 1,500, 3,000, 5,000, 7,500, 10,500, and lated by spline fitting to smoothen the 14,000km. Although the directional correlostraight line connections of the pixels. Con- grams are radially symmetric, for ease of tour lines of convenience were chosen for the interpretation they are represented here as six different variables; they are labeled in complete circular sets of coefficients. Correlations between pairs of dermatothe figures displaying the maps. To investigate the geographic variation glyphic variables over the 144 localities patterns, we employed the various tech- were computed as product-moment correlaniques developed in this laboratory for the tions. Because two of the variables, T and analysis of spatial variation in biological AIT, lacked observations at six and four lodata. These methods, termed spatial auto- calities, respectively, with the four a proper correlation analysis, have been summarized subset of the six, correlations involving in Sokal and Oden (1978a,b), Sokal and these variables were based on 138 or 140 Wartenberg (19811, Sokal (1979, 1986a,b), localities, respectively. Similarities between and Sokal and Jacquez (1991).Values of one nondirectional correlograms were estimated variable are said to be spatially autocorre- by computing their average Manhattan dislated when there is positive or negative as- tances (Sneath and Sokal, 1973) based on sociation of their values, as measured in the number of distance classes. Clustering pairs of localities separated by a certain dis- of the correlation matrix among surfaces, tance. Two commonly used measures of this and of the Manhattan distance matrix association are Moran’sI, a product-moment among correlograms, was carried out by UPcoefficient, and Geary’sc , a distance-like co- GMA hierarchic clustering (Sneath and efficient (Sokal and Oden, 1978a,b;Cliff and Sokal, 1973). Ord, 1981;Upton and Fingleton, 1985).Both RESULTS were employed and considered in this study; The patterns of the six dermatoglyphic to conserve space, only the values of Moran’s I are graphed. For large samples, values of I variables can be examined in Figures 2 range from approximately 1 (for positive through 7. PI1 exhibits a general trend in-

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creasing from west to east (see Fig. 2). Exceptions to this regular pattern are the U1chi and Udihe near Chabarovsk, Russia, who have substantially lower values of PI1 than their surrounding areas. The same is true for the Tuvians from Kyzyl, Russia. The lowest values on the map are found in the northwest where Komi, Saami, Finns, and Karelians range from 11.3-12.4. The trend shown by MLI (Fig. 3) differs from that of the previous variable. It is lowest in the southeast of the area (East China and Tai-

wan) and increases in a northwesterly direction, being highest in Scandinavia. However, Saami from Finland form a pocket of low MLI values among their high-valued neighbors. Yemenis too are surrounded by populations with appreciably higher MLIs. The pattern of T (Fig. 41, represents still another trend, with lowest values in the Middle East and increasing values in a northerly direction. The isofrequency contours generally run east-west. The lowest values of T are found in Arabs on the Islands

R. S O W AND G. LIVSHITS

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Fig. 4.Contour map of frequency of the axial triradius (T). The heavy contours indicate intervals of 8.

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Fig. 5. Contour map of frequency of the hypothenar patterns (HY). The heavy contours indicate intervals of 8

of Abd-al-Kuri and Socotra off Yemen, with other low values for Bucharan Jews and Baluchis in Turkmenia. An unusually low pocket is formed by the Komi in the far north at Izma, Russia. The pattern for variable HY (see Fig. 5) shows a low in East Asia, with a tendency to increase to the west and north. It resembles that of MLI. The lowest values of HY are recorded for Taiwanese and Balinese (7.2 and 9.1, respectively), another relatively low value in a high region is that for Dolgans in

the Tajmyr Peninsula, Russia. The Buryats in Northern Mongolia form a relatively high pocket (33.3) among intermediate-valued neighbors. Variable AIT (Fig. 6) features a high in the Middle Eastern region, with decreasing values toward the north and east. It somewhat resembles the pattern of T but, unlike the latter, lacks contour lines extending east-west across the area. The Arab population of Abd-al-Kuri Island off Yemen is unusually high for AIT (62.5) and quite different from that of neighboring Socotra Is-


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Fig. 6. Contour map of frequency of the accessory interdigital triradii (AIT).The heavy contours indicate intervals of 8.

Fig. 7. Contour map of frequency of the thenar-interdigital 1 pattern (TW1). The heavy contours indicate intervals of 4.

land. Finally, TWI (Fig. 7) shows a very patchy, unclear pattern with no gradient noticeable. Isofrequency contours for the same value of the variable, say 11.6, occur in different, widely separated regions, e.g., Northeastern Siberia, Northcentral Siberia, Iran, and Pakistan. A population almost lacking TW1 are the Basques from southern France (0.9), while other low values are found for Komi in Murmansk (4.6), for two Saami populations from Finland (3.5, 2.81,

and for Buryats and Mongolians from Mongolia (1.7,4.9).An unusually high reading is 22.5 for Abd-al-Kuri Island. The spatial correlograms of the six variables are shown in Figure 8. All but that for TW1 are highly significant a t a Bonferroni probability of P < 0.0005. While the I-correlogram for TW1 is not significant, the c-correlogram for that variable does show a moderate clinal trend significant at P = 0.008. The spatial structure in all variables seems


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Fig. 8. Spatial correlograms of the six variables. Ordinate: Moran’s autocorrelation coefficient I . Abscissa: distance classes coded 1-10, representing upper class limits of 250,500,1,000,1,500,2,000,3,000, 4,000,6,000,8,000, and 13,036 km. The abbreviations of the variables are explained in the text.

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well established. At least the first five variables are clearly clinal, even though PI1 increases up to 1,000 km before declining monotonically for greater distances. Negative autocorrelation in these data does not appear before at least 4,000, and at most 6,000km. Fifty-one of the 60 autocorrelation coefficients are statistically significant, so there is little doubt about the overall significance of the spatial structure exhibited by these variables. The correlograms of Figure 8 do not indicate the directions of the spatial trends. These are shown by the directional correlograms of Figure 9. All directional correlograms are Bonferroni significant at P < 0.00165. Variable PI1 shows a very clear clinal trend from WNW-ESE and a similar, only slightly more diffused trend is shown by MLI. The pattern of T is less clear, but indicates a NE-SW gradient, whereas the two-dimensional correlogram for HY is a well defined WNW-ESE gradient. The gradient is NE-SW for AIT, and no gradient whatsoever is apparent for TW1. Clustering the one-dimensional correlograms results in a cluster comprising correlograms for MLI and HY, with AIT joining that cluster. Variable T appears to have some affiliation with AIT and variable PI1 has some general overall relationship to the cluster of the previous four variables. Variable TW1 is quite distinct from these other clinal correlograms. The patterns of correlations among the six surfaces largely match those of the correlograms. There is one cluster of MLI with HY formed at 0.603 and another one between T and AIT at 0.510. Again, PI1 has low overall correlations with this cluster, ranging from 0.206-0.484. Variable TW1 is again quite isolated; its highest single correlation with AIT being 0.323.

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pattern, increasing from NW to SE for PII, and trending in the opposite direction for MLI and HY. There is some indication that the specific pattern for PI1 differs more from that of the other two than they do from each other. The second pattern is from S to N and E, increasing for T and decreasing for AIT. The lowest or highest value is in the Middle East. The third pattern is the patchy variation observed for TW1, which appears to be significant based on the directional correlogram (Fig. 9). The techniques for making inferences in such cases developed by Sokal and Oden (1978a), Sokal and Wartenberg (1981), Sokal (1986a,b), and Sokal and Jacquez (1991) indicate that the overall variation pattern is not dominated by a random process, otherwise the correlograms would have been quite similar to each other, and the surfaces dissimilar. What appears more likely is that we have two pairs of variables, each pair reflecting separate trends, and some evidence of a third variable (PII) showing yet a third trend. Whether the pattern observed in variable TW1, which was questionably significant, is worthy of note is difficult to say. It is sufficiently complex so that an explanation of its pattern is likely t o elude us. An explanation postulating different selection gradients for the three groups of variables is formally possible, but is unlikely. There is no creditable evidence for the adaptive significance of specific finger ridge patterns, and to explain adaptive gradients in such patterns seems pointless. We are left with a possible model of gene flow from centers in the Far East and Middle East, respectively, creating a gradient against the background of other Eurasian populations which differed in their values for the variables under consideration. The diffusion from the Far East is consistent with other DISCUSSION findings (Alekseev and Gochman, 1983) These findings are summarized in Table 1 showing a gradual diminution of Mongolid for the six variables. The picture that traits as one samples populations advancing emerges is an unusually clear one; the in a westerly direction across Eurasia. It is trends as shown in maps and two-dimen- difficult to know whether the diffusions are sional correlograms agree closely. The pat- the result of secondary contact between difterns are not identical, and there are at least ferentiated Mongoloid and Caucasoid poputwo, and probably three, distinct patterns of lations. Alternatively, the gradient could variation identified by maps, as well as cor- have resulted from the absorption of prerelograms. These are first a general NW-SE Mongolid elements by Mongolid populations


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Fig. 9. Directional spatial correlograms of the six variables. The annuli represent distance classes with upper limits of 500, 1,500, 3,000, 5,000, 7,500, 10,500, and 14,000 km. The shading represents approximate quintiles of all autocorrelation coefficients as follows: no shading -1,4240 to -0.4117; light gray -0.4113 to -0.1106; medium gray -0.1084 to 0.0645; dark gray 0.0666 to 0.2361; black 0.2391 to 0.9980. The abbreviations of the variables are explained in the text.

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TABLE 1. Oueruiew of results Variable

PI1 MLI

T

Number of samples 144 144 138

Contour map patterns W-E

SE-NW ME-NandE fS-N and E) SE-NW NandE-ME (N and E-S)

Surface clusters

Correlogram clusters

(HY, MLI, AIT, T ) HY AIT

C(HY, MLI, AIT, T) C(HY) C(AIT)

WNW-ESE WNW-ESE NE-SW

NW-SE SE-NW S-N and E

MLI T

C(ML1) C(HY, MLI)

WNW-ESE NE-SW

SE-NW N and E-S

isolated

ns

P

P

-

Directional correlograms

Summary

-

HY AIT

144 140

TW1

144

P

Notes: Contour mappatterns: In addition to conventional compass direction abbreviations, ME stands for Middle East and P for patchy. Arrows indicate direction of increasing values of the variable. Correlogram clusters: C, cline clustered with that of variableh) in parentheses. ns, not significant. Directional carrelagrams and Summary: Abbreviations as defined for contour map patterns.

TABLE 2. Within-sample correlations between pairs of the six dermatoglyphic variables (according to Heet, 19831, contrasted with among-sample correlations for the present samples

Pairs of traits

FTI, MLI F'TI, T F'TI, HY F'TI, AIT F'TI, TW1 MLI, T MLI, HY MLI, AIT ML1,TWl T, HY T, AIT T, TW1 HY, AIT HY, TW1 AIT, TW1

Armenians (N = 100)

Belarus (N = 100)

0.059 0.001 -0.038 0.002 -0.055 -0.049 -0.048 -0.008 -0.107 -0.213 -0.004 0.057 -0.062 -0.076 0.047

0.050 -0.079 0.020

-0.110 0,100 0.188 -0.070 0.030 0.060 -0.191 -0,090 -0.256 0.020 -0.020 0.108

Population sample Komi Finns Yakuts (N = 86) (N = 100) (N = 100) 0.068

-0.098 0.142 -0.072 -0.007 0.067 -0.019 -0.056 0.072 -0.416 -0.122 0.059 0.024 0.038 0.151

-0.001 0.094 -0.003 0.098 -0.137 0.059 -0.016 0.071 0.069 -0.136 0.066 0.060 -0.040 -0.024 0.156

expanding from the east as suggested by von Eickstedt (1934).The diffusion from a center in the Middle East is less clear; over the last two to three millenia, there are no recorded major population movements in the directions suggested by the patterns found by us. However, a general radiation in connection with the origin of agriculture from its Middle Eastern foci has been postulated by Renfrew (1991).Assuming such an origin for the patterns of T and AIT would make them very old, indeed. As we show in Table 2, for seven selected populations, there are very few appreciable correlations within populations on the average for the dermatoglyphic variables studied here. Yet we find that there are substantially higher correlations for the same variables among populations. To explain the lack of correlations within populations one

-0.004 0.038 0.007 -0.008 -0.061 -0.205 0.022 0.174 0.013 -0.137 -0.023 0.017 -0.099 -0.117 -0.134

Koreans (N = 107)

Russians (N = 103)

0.027 0.102 0.025 -0.093 0.050 -0.077 0.010 0.108 -0.013 0.063 -0.099 0.082 0.107 -0.055 0.000

-0.066 0.100 0.098 0.000 -0.167 -0.015 -0.021 -0.029 -0.148 -0,009 0.087 -0.069 -0.077 0.074

-0.020

Among samples Present study (N = 144) -0.484 0.331 -0.445 -0.206 -0.012 -0.372 0.603 0.561 0.242 -0.477 -0.510 0.041 0.457 0.026 0.323

must assume that the genetic factors and the developmental paths affected by them are largely independent of each other. The notable correlations among localities can be explained by chance differences in the founding populations of various ethnic groups, and subsequent diffusion between the extreme populations at the ends of each gradient, which induce correlations simply as a result of the diffusion process and not as the result of some deep underlying biological correlation. In this respect the results from the correlations support the earlier inferences by spatial autocorrelation analysis. ACKNOWLEDGMENTS Contribution No. 828 in Ecology and Evolution from the State University of New York at Stony Brook. We are indebted t o Paul Neal, Chester Wilson, and Donna Di-


404

Giovanni for technical assistance with this study. ~i~~~ Cachia competently wordprocessed the manuscript* This work was carr i d out with the support of grant GM28262 from the National Institutes of Health to R.S. and sabbatical leave funds from Tel Aviv University to G.L. LITERATURE CITED Alekseev W, and Gochman I1 (1983) Physical anthropology of Soviet Asia. In I Schwidetzky (ed.): Rassengeschichte der Menschheit Lfg. 9. Asien: 2, Sowjet Asien. Munich: Oldenbourg, pp. 5-186. Borecki IB, Malhotra KC, Mathew S, Vijayakumar M, Poosha DVR, and Rao DC (1985) A family study of dermatoglyphic traits in India: Resolution of genetic and uterine environmental effects for palmar-pattern ridge counts. Am. J. Phys. Anthropol. 68r417-424. Castilla AQ (1979) Dermatoglyphic study in Spanish penal population. In W. Wertelecki, CC Plato, and NW Paul (eds.1: Dermatoglyphics Fifty Years Later. New York: Alan R. Liss Inc., pp. 411-416. Chakraborty R (1990) Quantitative traits in relation to population structure: Why and how are they used and what do they imply? Hum. Biol. 62t147-162. Chopra W (1979) The inheritance of dermatoglyphics: A factor analytic approach. Homo 30r1-8. Cliff AD, and Ord J K (1981) Spatial Processes. London: Pion. Dow MM, Cheverud JM, and Friedlaender JS (1987) Partial correlation of distance matrices in studies of population structure.Am. J. Phys. Anthropol. 72r343352. Froelich JW (1976) The quantitative genetics of fingerprints. In E Giles and J Friedlaender (eds.): The Measures of Man. Cambridge, MA: Harvard University Press, pp. 260-320. Gualdi-Russo E (1987) Palmar dermatoglyphics in a sample of Italian population. Int. J . Anthropol. 2t105116. Gualdi-RussoE, Zannotti M, and Cenni S (1982) Digital dermatoglyphics in Italians. Hum. Biol. 54r373-386. Heet HL (1983) Dermatoglyphics of the USSR Peoples. Moscow: Nauka (in Russian). Heet HL, and Dolinova NA (1990) Racial Differentiation of Mankind. Dermatoglyphic Data. Moscow: Nauka. Heet HL, and Keita B (1979) Dermatoglyphic divergence of the main racial branches of mankind. In W Wertelecki, CC Plato, and NW Paul (eds.): Dermatoglyphics Fifty Years Later. New York Alan R. Liss Inc., pp. 249-260. Hosebe K 11918) Uber das Hautleistensystem der Vola und Planta der Japaner und Ainu. Arb. Anat. Inst. Sendai lt13-88. Jantz RL (1979) On the level of dermatoglyphic variation. In W Wertelecki, CC Plato, and NW Paul (eds.): Dermatoglyphics Fifty Years Later. New York: Alan R. Liss Inc., pp. 53-61. Kamali MS, Malhotra KC, and Chakraborty R (1986) Diversity in palmar pattern ridge counts among 12 Iranian populations.Am. J. Phys. Anthropol. 70t443-455.

Kamali MS, and Mavalwala J (1990)Diversity of palmar pattern ridge counts in Iranian populations. Am. J. Phys. Anthropol. 81:363-373. Katayama K (1981) Genetic study of hypothenar patterns on the palm: Estimation of the heritability of liability. Jpn. J. Hum. Genet. 26t279-288. Katayama K (1982) Genetic studies in Tobishima: 11 Dermatoglyphic differentiation among the villages. J. Anthropol. SOC. Nippon, 90t269-290. Keith HH (1924)Racial differences in the papillary lines of the palm. Am. J . Phys. Anthropol. 7t165-206. Loesch D (1983) Quantitative dermatoglyphics; classification, genetics and pathology. Oxford: Oxford University Press. Luna F, and Pons J (1987)The dermatoglyphics of Eastern Andalusia. Int. J . Anthropol. 2:183-190. Mavalwala J (1977) Dermatoglyphics. An International Bibliography. The Hague: Mouton. Meier R J (1980)Anthropological dermatoglyphics: A review. Yearbk. Phys. Anthropol. 23r147-178. Oden NL (1984) Assessing the significance of a spatial correlogram. Geogr. Anal. 16:l-16. Oden NL, and Sokal RR (1986) Directional autocorrelation: An extension of spatial correlograms to two dimensions. Syst. Zool. 35t606-617. Piazza A, Menozzi P, and Cavalli-Sforza LL (1981) Synthetic gene frequency maps of man and selective effects of climate. Proc. Natl. Acad. Sci. USA 78~26382642. Plato CC, Cereghino J J , and Steinberg FS (1975) The dermatoglyphics of American Caucasians. Am. J. Phys. Anthropol. 42t195-210. Pollitzer WS, and CC Plato (1979) Anthropology and dermatoglyphics. In W Wertelecki, CC Plato, and NW Paul (eds.): Dermatoglyphics Fifty Years Later. New York: Alan R. Liss Inc., pp. 211-223. Pons J (1990) The multivariate analysis of dermatoglyphics in population systematics. Int. J. Anthropol. 5r227-234. Rand McNally (1980)The New International Atlas. Chicago: Rand McNally & Co. Renfrew C (1991) Before Babel: Speculations on the origins of linguistic diversity. Cambr. Archaeol. J. 1t323. Roberts DF (1979) Dermatoglyphics and human genetics. In W Wertelecki, CC Plato, and NW Paul (eds.): Dermatoglyphics Fifty Years Later. New York: Alan R. Liss Inc., pp. 475-494. Rothhammer FR, Chakraborty R, and Llop E (1977) A collation of marker gene and dermatoglyphic diversity at various levels of population differentiation. Am. J. Phys. Anthropol. 46t51-59. Schwidetzky I (1962) Die Neue Rassenkunde. Stuttgart: Gustav Fischer Verlag. Schwidetzky I (1974) Grundlagen der Rassensystematik. Mannheim: Bibliographisches Institut. Sneath PHA, and Sokal RR (1973) Numerical Taxonomy. San Francisco: W.H. Freeman, Sokal RR (1979) Testing statistical significance of geographical variation patterns. Syst. Zool. 28:227-231. Sokal RR (1986a) Die raumliche Analyse der menschlichen Populationsstruktur. Homo 37t50-71.

DERMATOGLYPHIC TRAITS Sokal RR (198613) Spatial data analysis and historical processes. In E Diday, Y Escoufier, L Lebart, J Pages, Y Schecktman, and R Tomassone (eds.): Data Analysis and Informatics, IV. Amsterdam: North-Holland, pp. 29-43. Sokal RR, and Friedlaender J (1982) Spatial autocorrelation analysis of biological variation on Bougainville Island. In MH Crawford and Mielke J H (eds.): Current Developments in Anthropological Genetics, Vol. 2. New York: Plenum, pp. 205-277. Sokal RR, Harding RM, and Oden NL (1989) Spatial patterns of human gene frequencies in Europe. Am. J . Phys. Anthropol. 80.267-294. Sokal RR, and Jacquez GM (1991) Testing inferences about microevolutionary processes by means of spatial autocorrelation analysis. Evolution 45t152-168. Sokal RR, and Oden NL (1978a) Spatial autocorrelation in biology 1. Methodology. Biol. J. Linn. SOC. 10t199228. Sokal RR, and Oden NL (1978b) Spatial autocorrelation in biology 2. Some biological implications and four applications of evolutionary and ecological interest. Biol. J. Linn. SOC.10:229-249. Sokal RR, and Uytterschaut H (1987) Cranial variation

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in European populations: A spatial autocorrelation study a t 3 time periods. Am. J. Phys. Anthropol. 74:21-38. Sokal RR, and Wartenberg DE (1981) Space and population structure. In D Griffith and R McKinnon (eds.): Dynamic Spatial Models. Alphen aan den Rijn, The Netherlands: Sijthoff and Noordhoff, pp. 186-213. Sokal RR, and Winkler E-M (1987) Spatial variation among Kenyan tribes and subtribes. Hum. Biol. 59:121-145. Thoma A (1974) Dermatoglyphics and the origin of races. J. Hum. Evol. 3:241-245. Upton GJG, and Fingelton B (1985) Spatial Data Analysis by Example. Vol. I. Point Pattern and Quantitative Data. Chichester, England: John Wiley. von Eickstedt E (1934) Rassenkunde und Rassengeschichte der Menschheit. Stuttgart: Gustav Fischer. Wilder HH (1904) Racial differences in palm and sole configurations. Am. Anthropol. 6.244-295. Wilder HH (1913) Racial differences in palm and sole configurations. 11. Palm and sole prints of Liberian natives. Am. Anthropol. 15:189-207.

APPENDIX A. List o f the studied DoDulations

Ethnic ~. group

No. ~

001 002 003

004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039

~

NGANASAN DOLGANS NENETS ENTSI (Enets) KOMI S M I SAAMI SAAMI-SKOLT NENETS ESKIMOS CHUKCHI FINNS SELKUPS FINNS KOMI RUSSIANS KARELIANS MANS1 KORYAK YAKUTS KHANTS MANS1 FINNS SWEDES SWEDES VEPSES ESTONIANS EVENS SWEDES RUSSIANS RUSSIANS LATVIANS UDMURTS KYZYLS MARIS CHWASH KETS MARIS RUSSIANS ~~~~

Coordinates 76.0000 71.0000 70.0000 69.4167 68.9667 68.9000 68.0015 68.0013 68.0000 66.1667 66.0000 65.9667 65.9333 65.7667 65.0500 64.5333 63.9500 63.9333 62.5000 62.2167 61.0000 60.7000 60.6667 60.5000 60.2500 59.9167 59.4100 59.3833 59.3333 58.0000 57.7667 56.9500 56.8500 56.7667 56.6333 56.0015 56.0000 55.9667 55.8167

104.0000 94.4700 70.0000 86.2500 33.0008 27.0002 33.2500 27.0013 45.0000 -169,8000 -171.0000 29.1833 87.7000 24.5667 53.8000 40.5333 31.9200 65.0333 172.0000 129.8167 69.1000 60.4000 22.5000 25.0000 20.3333 30.2500 24.7500 143.3000 18.0500 31.3833 40.9167 24.1000 53.2333 88.9800 47.8667 47.2500 94.0000 48.0333 38.9833

Country and geographic location

Source of the information

USSR, Tajmyr Peninsula+ USSR, Volocanka, Tajmyr USSR, Jamal Peninsula USSR. Dudinka. Ust-Jenisei. Jenisei River USSR; Lovozersk, Murmansk FINLAND, Inari USSR, Olenegorsk FINLAND, Viotso USSR, Kanin Peninsula" USSR, nr Uelen, Chukchi Peninsula USSR, Chukchi Peninsula" FINLAND, Kuusamo USSR, Turuchan, Krasnojarsk FINLAND, Ulitorni USSR, Izma USSR, Archangelsk USSR, Mujezerskij USSR, Berozovo, Chanty-Mansijsk USSR, Koriak National DistrictUSSR, Yakutsk USSR, Chanty-Mansijsk USSR, Ivdelsk, nr Sverdlovsk FINLAND, Varsinais-Suomi FINLAND, Uusima FINLAND, &and Islands USSR, Leningrad USSR, Tallinn" USSR, Ochotsk SWEDEN, Stockholm* USSR, Staraja Russa USSR, Kostroma USSR, Riga" USSR, Izevsk* USSR, Ordzonikidze area USSR, Joskar-Ola" USSR, Cheboksary USSR, nr Krasnojarsk USSR, Zvenigovo USSR. Orechovo-Zuievo

H&D; H H&D; H H H H H H&D H&D; H H H&D H&D H H H H H H&D H H&D H H&D; H H H H H H&D H&D H H&D H H H&D H HCD; H H H H&D; H H H (continued)


406

APPENDIX A. List of the studied populations (Continued)

Ethnic group

No.

Coordinates



~

040 04 1 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056

KACHINS TATARS TELEUTS BASHKIRS-KATAITZ BASHKIRS-TABINZ EVENKS LITHUANIANS RUSSIANS MORDVINS SAGAI(AN1

057 058 059

CHULIMS GERMANS TUBALAR (Tofalars, Karaga) BURYAT-BULATS POLES

060 061 062 063 064 065 066 067

068 069 070 071 072 073 074 075 076 077 078

NANAT . .- .-

BELORUSSIANS KUMANDINS SHORS BELTIRE NIVCHIS (Gilyaks) BASHKIRS-TANIPZ

MAR.TS ~

ALTAIKIZHI TUVIANS CHUVASH RUSSIANS UKRAINIANS BASHKIRS BELGIANS TELENGITS BURYATS KIRGHIZ CZECHS UKRAINIANS

UKRAINIANS OROCHI FRENCH BURYATS

079

ULCHI

080 081 082 083 084 085 086 087 088 089

UDIHE SLOVAKS MONGOLIANS HUNGARIANS MOLDAVIANS KOREANS KALMYKS ROMANIANS CHERKESSY KARACHAY

090 091 092 093 094 095

ABAZINIANS BASQUES KABARDIANS CHECHENS BALKARS NOGAIS

096 097 098

OSSETIANS ABKHAZIANS

LAKS

55.7667 55.7500 55.3333 55.0167 54.7333 54.7000 54.6667 54.6333 54.1833 54.1667 54.0000 53.9000 53.8833 53.7500 53.5000 53.1333 52.8167! (52.7167) 52.7167 52.5500 52.3667! (52.2667) 52.2667 52.2500 52.0667 51.9667 51.7000 51.6000 51.5667 51.5000 51.3833 50.8333 50.6333 50.6000 50.2167 50.0833 49.8333 49.3833 48.9667 48.8667 48.7667! (48.8667) 48.5500! (48.4500) 48.4500 48.1500 47.9167 47.5000 47.0000 46.9667 46.5000 44.4333 44.2333 43.8500! (43.7500) 43.7500 43.6833 43.4833 43.3333 43.3200 43.1000! (43.0000) 43.0500 43.0167 43.0000

USSR, Ordzonikidze area USSR, Kazan USSR. Kemerovo USSR: Asa USSR, Ufa USSR, Chumikan, Chabarovsk USSR, Vilnius* USSR, Ryazan USSR, Saransk* USSR, Tashtip USSR, Chabarovsk USSR, Minsk* USSR, Ozerki, (Altai) Krasnogorsk USSR, Novokuznezk* USSR, Khakass, (nr Abakan) USSR, Nikolajevsk-na-Amure USSR, Jermolajevo

H&D; H H&D H&D; H H H H&D H&D H H&D H&D H&D H&D H H&D H&D H&D H

USSR, Tambov GERMANY, Berlin* USSR, Turuchansk

H&D; H H&D H&D; H

104.3333 21.0000 47.3667 85.9667 94.4500 45.9167 46.0333 31.3000 57.6000 4.3333 87.9667 107.5833 96.4500 14.4333 24.0000 33.2833 140.3000 2.3333 103.5367

USSR, Irkutsk POLAND, Warsaw* USSR, Volsk USSR, Gorno-Altaisk USSR, Kyzyl USSR, Churachiki, nr Saratov USSR, Saratov USSR, Chernigov USSR, Usergan BELGIUM, Brussels" USSR, Ust-Ulagan USSR, Bicura, Buryat ASSR USSR, Naryn CZECHOSLOVAKIA, Prague* USSR, Lvov USSR, Globino USSR, Sovgansk FRANCE, Paris* MONGOLIA, Northern part, Bulgan

H H&D H H&D H&D H H H H H&D H&D; H H H H&D H H H&D; H H&D H

135.1000

USSR, Ulch, Chabarovsk

H&D; H

135.1000 17.1167 106.8833 19.0833 28.8333 142.70 45.5000 26.1000 42.0667 41.9000

USSR, Lazovsk, Chabarovsk CZECHOSLOVAKIA, Bratislava" MONGOLIA, Ulan Bator* HUNGARY, Budapest* USSR, Kishinev* USSR, Anivsk, Juzno-Sachalinsk USSR, Kalmyk ASSR* ROMANIA, Bucharest* USSR, Cherkessk* USSR, Karacjajevsk*

H&D; H H&D H&D H&D H H&D H&D H&D H&D

41.9000 4.8667 43.6167 45.7000 43.6000 47.0000

USSR, Karacjajevsk* FRANCE, Mouries USSR, Nal'cik* USSR, Groznyj USSR, Sovetskoje USSR, Karanogaisk

H&D H&D H&D H&D H&D; H H&D; H

44.6667 41.0333 47.0000

USSR, Ordzhonikidze USSR, Sukhumi USSR. between Lak and Kuli

H&D H&D H&D

88.9800 49.1333 86.0833 57.3000 55.9333 135.3167 25.3167 39.7333 45.1833 90.0000 136.0000 27.5667 83.7333 87.1000 90.0000 140.7333 55.8000 41.4167 13.3000 87.1333

H&D

(continued)

DERMATOGLYPHICTRAITS

407

APPENDIX A. List of the studied populations (Continued)

No.

Ethnic zrouu

099 100 101 102 103 104 105 106 107 108 109 110 111

TURKMENS UZBEKS KUMYKS BULGARS MACEDONIANS ITALIANS GEORGIANS UZBEKS UZBEKS PAMIRS SPANIARDS TADZHIKS AZERIS

112 113 114 115 116

YAGNOBS ARMENIANS KIRGIZ TURKS CHINESE

117 118 119 120

JEWS TALYSH TADZHIKS GREEKS

121 122 123 124 125 126

RUSHANIS TURKMENS SHUGNANS KOREANS BALUCHI KURDS

127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144

DUNGANS JAPANESE PERSIANS DUNGANS TIBETANS INDIANS NEPALESE TAIWANESE ARABS VIETNAMESE THAIS YEMENIS THAIS SOCOTRA ARABS KAMPUCHEANS KHMER BALI

Coordinates -~ _ _ _ _ 42.9500 42.9000 42.8167 42.6833 41.9833 41.9000 41.7167 41.3333 41.0000 40.4667 40.4000 40.3833 40.3033! (40.38331 40.2833 40.1833 40.1167 39.9167 39.8167! (39.9167) 39.8000 38.7500 38.5833 38.0667! (37.9667) 37.9667 37.9500 37.5167 37.5000 37.3000 37.0000 36.0500 35.7000 35.6667 34.2500 29.6667 28.4728 27.7167 25.0500 24.6333 21.0333 18.7833 15.3833 13.7500 12.5000 12.2000 12.0000 11.5500 -8.3333 -



59.7833 74.6000 47.1167 23.3167 21.4333 12.4833 44.8167 69.3000 71.6667 71.7333 -3.6833 71.7667 49.8500

USSR, Cimbaj USSR, Frunze USSR, Bujnaksk BULGARIA, Sofia YUGOSLAVIA, Skopje* ITALY, Rome* USSR, Tbilisi* USSR, Tashkent USSR, Namangan USSR, Margilan* SPAIN, Madrid USSR, Fergana USSR, Baku*

H H H&D; H H&D H&D G-R; GR H H H H&D c; L&P H H&D

69.6167 44.5000 71.7333 32.8333 116.4167

USSR, Leninabad* USSR, Jerevan* USSR, Frunze region TURKEY, Ankara* CHINA, Beijing*

H&D H&D H H&D H&D

USSR, Bukhara USSR, Lenkoran* USSR, Dushanbe GREECE, Athens*

H&D H&D H H&D

USSR, Rusan USSR, Aschabad USSR, Horog KOREA, Seoul" USSR, Iolotan, Turkmenian SSR KURDISTAN" (on Turkish-Iranian-Iraqi Border) CHINA, Gansu, Lanzhou JAPAN, Tokyo* IRAN, Teheran" CHINA, Shaanxi, Xi'an CHINA, Lhasa' INDIA, Delhi" NEPAL, Katmandu* TAIWAN. Taiuei* SAUDI ARAB'IA, Riyadh* VIETNAM, Hanoi* THAILAND, Chiang Mai YEMEN, San'a THAILAND, Bangkok* YEMEN, Socotra Island YEMEN. Abd-Al-Kuri Island KAMPUCHEA, Kampong Cham* KAMPUCHEA, Phnom Penh* INDONESIA, Bali*

H H H H&D H H&D

64.4167 48.8333 68.8000 23.7167 71.5000 58.4000 71.5500 127.0000 62.3500 45.0000 103.6833 139.7667 51.4333 108.8667 91.1500 77.1667 85.3167 121.6667 46.7167 105.8500 98.9833 44.2000 100.5167 54.0000 52.2500 105.4500 104.9167 115.0000 ~~

H H&D; K H&D H H&D P H&D H&D H&D H&D H&D H&D H&D H&D H&D H&D H&D H&D

'Geographic coordinates are for latitudes north followed by longitudes east. Negative latitudes indicate south, negative lonatudes west. Symbols: = approximate geographic coordinates; (!I = minor change introduced to avoid duplicated coordinates during the computations (actual value given in parentheses); (*) = capital or main city in the area; no more precise information was available. H, H&D, C, G-R, GR, L&P, K, and P indicate that the description of the population and data come from Heet (1983), Heet and Dolinova (19901, Castilla (19791, Gualdi-Russo et al. (19821, Gualdi-Russo (1987), Luna and Pons (19871, Katayama (19821, and Plato et al. 119751, respectively. The list does not reflect the recent political changes in the Soviet Union and Eastern Europe since we wanted the localities to reflect faithfully the original citations.

(+I