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. 2010 Jan 20;5(1):e8783. doi: 10.1371/journal.pone.0008783

Dermatoglyphics from All Chinese Ethnic Groups Reveal Geographic Patterning

Hai-Guo Zhang 1,*,#, Yao-Fong Chen 2,#, Ming Ding 3,#, Li Jin 4, D Troy Case 5, Yun-Ping Jiao 3, Xian-Ping Wang 6, Chong-Xian Bai 7, Gang Jin 8, Jiang-Ming Yang 9, Han Wang 9, Jian-Bing Yuan 10, Wei Huang 11, Zhu-Gang Wang 1, Ren-Biao Chen 1
Editor: Lyle Konigsberg12
PMCID: PMC2808343  PMID: 20098698

Abstract

Completion of a survey of dermatoglyphic variables for all ethnic groups in an ethnically diverse country like China is a huge research project, and an achievement that anthropological and dermatoglyphic scholars in the country could once only dream of. However, through the endeavors of scientists in China over the last 30 years, the dream has become reality. This paper reports the results of a comprehensive analysis of dermatoglyphics from all ethnic groups in China. Using cluster analysis and principal component analysis of dermatoglyphics, it has been found that Chinese populations can be generally divided into a southern group and a northern group. Furthermore, there has been considerable debate about the origins of many Chinese populations and about proper assignment of these peoples to larger ethnic groups. In this paper, we suggest that dermatoglyphic data can inform these debates by helping to classify a Chinese population as a northern or southern group, using selected reference populations and quantitative methods. This study is the first to assemble and investigate dermatoglyphics from all 56 Chinese ethnic groups. It is fortunate that data on population dermatoglyphics, a field of physical anthropology, have now been collected for all 56 Chinese ethnic groups, because intermarriage between individuals from different Chinese ethnic groups occurs more frequently in recent times, making population dermatoglyphic research an ever more challenging field of inquiry.

Introduction

Each person's set of fingerprints is different, but fingerprints for an individual remain stable over a lifetime. These characteristics have made fingerprints very useful as tools for law enforcement officials in many criminal cases. Fingerprints also vary considerably among different groups of people, and can be useful as tools for tracing individuals to particular populations. Because fingerprints are highly variable and genetically influenced, they have important significance for forensic science, anthropology, ethnology, genetics, and medicine [1], [3], [4].

Population dermatoglyphics is a field of research within physical anthropology. It focuses on the dermatoglyphics of different ethnic groups [1]–[4]. The investigation of population dermatoglyphics in China began in 1910 (Taiwan), and a total of more than fifty papers on dermatoglyphics were published prior to 1971, though they reported on only a limited number of dermatoglyphic variables [5]. Only a small number of research projects on dermatoglyphics were carried out in Mainland China before 1964, and large-scale investigation and research on dermatoglyphics did not begin until 1977 [6]–[10]. Over the past 30 years, through the endeavors of many dermatoglyphic researchers in China, we have jointly completed a grand research project on the dermatoglyphics of the Chinese people [6]–[27].

China has a population of 1.3 billion people, and a total of 56 different ethnic groups are recognized in the country [6], [10], [11], [16], [19]. The Han Chinese group has the greatest population with 1.2 billion members. We have now successfully completed an investigation and analysis of dermatoglyphics for all 56 Chinese ethnic groups. One result of this study has been a recognition that dermatoglyphics among Han Chinese show strong diversities.

Table 1 lists the geographic area, sample size and published references for all populations studied in China [6]–[30]. If a sample's abbreviation has an asterisk “*” after the name, it is a combined sample. In our study, an ethnic group may have samples from several populations, and the data from these populations are combined into one sample. The complete dataset of dermatoglyphic variables for the Chinese ethnic groups are listed in Table 2. This study is the first complete and comprehensive dermatoglyphic research for all 56 Chinese ethnic groups. It is fortunate that data on population dermatoglyphics have now been collected for all 56 Chinese ethnic groups, because intermarriage between individuals from different ethnic groups is more frequent in recent times, making population dermatoglyphic research an ever more challenging field of inquiry.

Table 1. Geographical area, sample size and references for samples and dermatoglyphic variables of Chinese samples and outgroups.

Ethnic group Abbrev-iation Province/Country North latit-ude East Long-tude Males Females Total TFRC a-bRC A Lu Lr W T/I II III IV H Ref.
Achang Achang-1 Yunnan 24.4 97.9 231 236 467 134.87 36.66 2.72 43.64 2.38 51.26 4.71 1.61 8.68 61.14 12.63 [11]
Achang-2 Yunnan 24.8 98.2 287 290 577 133.07 38.73 3.31 52.37 2.79 41.53 5.37 1.21 17.42 77.30 15.17 [6]
Achang-* 1044 133.88 37.80 3.05 48.46 2.61 45.88 5.07 1.39 13.51 70.07 14.03
Bai Bai----1 Yunnan 25.6 100.1 400 400 800 125.97 35.21 2.36 49.37 3.05 45.22 1.01 0.57 15.76 79.57 12.25 [11]
Bai----2 Yunnan 26.0 99.9 500 500 1000 130.12 36.72 1.55 48.64 2.96 46.85 5.35 0.30 15.40 77.30 16.45 [6]
Bai----* 1800 128.28 36.05 1.91 48.96 3.00 46.13 3.42 0.42 15.56 78.31 14.58
Blang Blang--1 Yunnan 22.0 100.8 187 204 391 132.68 34.96 2.29 51.84 2.72 43.15 2.67 1.02 14.39 82.68 14.64 [11]
Blang--2 Yunnan 23.4 99.8 500 500 1000 125.55 33.81 1.72 51.33 1.52 45.43 2.75 0.95 9.20 71.00 12.70 [6]
Blang--* 1391 127.55 34.13 1.88 51.47 1.86 44.79 2.73 0.97 10.66 74.28 13.25
Bonan Bonan--1 Gansu 35.7 102.8 126 41 167 137.96 39.21 1.06 47.89 2.89 48.16 4.73 0.00 6.57 51.81 20.50 [16]
Bonan--2 Gansu 35.7 102.8 301 240 541 161.99 35.78 2.61 45.73 3.05 48.61 6.00 0.46 15.28 77.33 21.16 [16]
Bonan--* 708 156.32 36.59 2.25 46.24 3.01 48.50 5.70 0.35 13.23 71.31 21.00
Bouyei Bouyei Guizhou 26.6 106.7 230 218 448 132.99 36.68 0.85 44.80 2.25 52.10 3.24 0.89 12.83 65.86 8.93 [16]
Dai Dai----1 Yunnan 24.4 97.9 300 300 600 130.00 38.25 2.38 47.92 2.27 47.43 1.25 0.75 13.83 72.58 10.67 [11]
Dai----2 Yunnan 24.4 97.9 500 507 1007 125.37 37.50 4.00 53.68 3.18 39.14 2.78 1.54 14.35 67.87 9.63 [6]
Dai----* 1607 127.10 37.78 3.39 51.53 2.84 42.24 2.21 1.25 14.16 69.63 10.02
Daur Daur Xinjiang 46.7 82.9 500 500 1000 144.29 37.25 2.46 44.81 3.16 49.57 3.40 1.85 24.50 57.05 17.30 [16]
De'ang De'ang-1 Yunnan 24.4 98.5 170 130 300 134.49 38.02 4.60 47.63 1.73 46.04 4.33 0.33 12.83 53.17 10.33 [11]
De'ang-2 Yunnan 24.4 98.5 330 260 590 125.33 36.79 4.16 50.59 3.47 41.78 4.83 0.42 13.31 69.75 12.46 [6]
De'ang-* 890 128.41 37.20 4.31 49.59 2.88 43.22 4.66 0.39 13.15 64.16 11.74
Derung Derung-1 Yunnan 27.7 98.6 100 98 198 124.35 34.60 4.14 44.19 6.26 45.41 4.55 0.50 12.88 78.28 8.59 [11]
Derung-2 Yunnan 27.7 98.6 136 164 300 127.20 36.47 4.80 48.80 7.87 38.53 6.17 0.33 11.50 70.00 9.17 [6]
Derung-* 498 126.07 35.73 4.54 46.97 7.23 41.26 5.53 0.40 12.05 73.29 8.94
Dong Dong---1 Guizhou 25.9 108.5 199 215 414 131.09 37.16 3.01 45.34 1.93 49.72 2.52 1.53 15.36 63.62 9.96 [16]
Dong---2 Guangxi 25.8 110.1 340 330 670 140.18 36.90 2.31 49.18 2.84 45.67 3.97 1.72 13.41 69.36 15.43 [11]
Dong---* 1084 136.71 37.00 2.58 47.71 2.49 47.22 3.42 1.65 14.15 67.17 13.34
Dongxiang DongX. Gansu 35.6 103.3 307 75 382 142.88 38.02 2.29 48.50 3.18 46.03 8.81 1.74 11.75 55.03 18.58 [16]
Ewenki Ewenki Inner Mongolia 49.1 119.7 317 306 623 147.67 36.36 2.24 44.78 2.36 50.62 6.99 1.62 7.16 25.86 19.72 [16]
Gaoshan GaoS.--1 Taiwan 23.6 121.6 50 50 100 162.21 40.20 1.20 38.50 2.60 57.70 8.00 0.00 14.50 79.00 14.50 [7]
GaoS.--2 Taiwan 23.7 121.4 100 100 200 163.07 39.12 1.25 40.80 2.35 55.60 9.00 0.50 17.50 68.00 11.75 [8]
GaoS.--* 300 162.78 39.48 1.24 40.03 2.43 56.30 8.67 0.33 16.50 71.67 12.67
Gelao Gelao Guizhou 27.7 106.9 209 201 410 135.95 37.33 2.02 46.83 2.39 48.76 4.41 2.82 18.75 65.04 8.70 [16]
Gin Gin----1 Guangxi 21.7 108.3 128 113 241 147.80 39.70 1.37 45.02 2.66 50.95 4.98 1.04 9.33 63.07 13.07 [11]
Gin----2 Guangxi 21.7 108.3 270 230 500 140.81 38.95 1.78 45.24 2.78 50.20 3.10 0.60 9.10 61.30 7.00 [16]
Gin----* 741 143.08 39.19 1.65 45.17 2.74 50.44 3.71 0.74 9.17 61.88 8.97
Han Han----1 Taiwan 25.0 121.5 100 100 200 151.26 39.38 2.15 43.95 2.40 51.50 5.75 3.00 20.50 70.50 19.50 [9]
Han----2 Taiwan 25.0 121.5 100 100 200 143.38 40.01 2.25 47.85 2.30 47.60 9.00 1.25 20.50 75.75 20.50 [10]
Han----3 Shanxi 33.6 109.1 134 133 267 102.40 32.34 3.71 43.61 2.32 50.36 11.28 1.13 6.02 62.97 13.16 [16]
Han----4 Anhui 31.3 118.4 220 162 382 136.29 37.23 2.88 45.11 2.17 49.84 4.84 2.62 14.14 65.32 14.13 [12]
Han----5 Guizhou 27.7 106.9 204 209 413 135.89 39.70 2.20 44.31 1.77 51.72 7.53 1.88 14.59 68.35 5.76 [11]
Han----6 Liaoning 41.1 121.1 250 250 500 126.34 33.60 3.64 48.32 2.62 45.42 5.50 2.20 5.20 65.50 10.10 [16]
Han----7 Shanghai 31.2 121.4 309 284 593 133.25 38.09 2.60 45.41 2.39 49.60 11.41 0.42 11.96 58.32 18.79 [13]
Han----8 Sichuan 28.8 105.4 367 327 694 150.97 38.96 2.33 44.99 2.58 50.10 8.27 0.92 11.58 56.18 11.44 [11]
Han----9 Inner Mongolia 49.0 119.0 456 456 912 127.86 31.35 2.10 47.67 2.64 47.59 4.35 3.26 21.14 77.94 11.89 [14]
Han---10 Shanghai 31.2 121.4 520 520 1040 143.63 38.05 2.05 44.65 2.44 50.86 8.67 0.87 14.66 73.46 17.26 [6]
Han---11 Jiangsu 34.2 117.1 582 508 1090 129.87 34.07 2.06 47.14 2.06 48.74 5.00 1.69 15.92 65.00 11.65 [16]
Han---12 Jiangsu 32.0 118.7 698 483 1181 128.22 38.53 2.21 45.11 3.20 49.48 8.89 1.61 15.63 66.70 12.31 [16]
Han---13 Shanghai 31.2 121.4 640 560 1200 131.10 36.90 0.90 43.90 2.70 52.50 3.00 2.15 14.40 68.35 11.30 [15]
Han---14 Tianjin 39.1 117.2 642 638 1280 141.92 40.04 1.88 46.87 2.44 48.81 10.62 2.07 18.44 74.05 17.69 [17]
Han---15 Shanghai 31.2 121.4 640 661 1301 126.77 35.77 3.45 43.65 2.54 50.36 10.14 1.69 14.72 65.95 16.33 [18]
Han----* 11253 133.68 36.83 2.29 45.49 2.51 49.71 7.60 1.77 15.05 68.01 13.94
Hani Hani---1 Yunnan 22.0 100.7 210 210 420 118.32 36.03 3.19 51.83 2.84 42.14 3.93 0.72 15.72 72.27 12.14 [11]
Hani---2 Yunnan 23.1 102.7 520 167 687 135.90 35.99 1.41 49.87 2.88 45.84 0.80 0.80 15.65 82.68 11.21 [19]
Hani---3 Yunnan 23.4 102.8 500 500 1000 137.57 38.49 2.54 51.88 2.57 43.01 6.90 0.70 14.35 79.05 20.65 [6]
Hani---* 2107 133.19 37.18 2.30 51.21 2.73 43.76 4.32 0.74 15.05 78.88 15.88
Hezhen Hezhen Heilongjiang 46.8 134.0 86 80 166 142.14 35.35 3.19 47.95 2.05 46.81 12.35 1.81 21.99 51.20 11.14 [16]
Hui Hui----1 Hainan 17.8 109.2 183 38 221 145.47 38.38 1.85 54.51 2.34 41.30 6.13 0.00 6.12 49.00 9.08 [16]
Hui----2 Anhui 33.8 115.7 200 200 400 138.79 37.12 2.60 49.87 2.38 45.15 7.75 1.00 19.25 69.75 15.00 [16]
Hui----3 Yunnan 24.1 102.7 200 200 400 130.03 36.28 3.10 47.20 1.85 47.85 4.13 0.63 11.25 53.88 10.63 [11]
Hui----4 Gansu 35.6 103.1 364 170 534 157.09 38.98 1.64 44.66 2.70 51.00 6.94 0.47 8.67 47.56 20.53 [16]
Hui----5 Inner Mongolia 40.8 111.7 309 411 720 128.10 36.00 2.75 49.34 2.38 45.53 4.97 1.84 16.48 62.57 15.98 [14]
Hui----6 Ningxia 38.4 106.2 431 500 931 127.25 36.79 4.40 48.50 2.20 44.90 5.90 0.10 26.20 80.80 19.70 [16]
Hui----7 Yunnan 25.5 103.2 500 500 1000 129.21 37.39 2.19 51.92 2.80 43.09 2.65 0.30 8.00 76.75 16.25 [6]
Hui----* 4206 133.97 37.14 2.81 49.29 2.43 45.47 5.12 0.62 14.85 67.21 16.48
Jingpo Jingpo-1 Yunnan 24.4 97.9 254 242 496 135.08 38.09 2.56 47.16 2.05 48.23 1.31 0.71 15.43 72.38 7.46 [11]
Jingpo-2 Yunnan 24.5 98.5 500 500 1000 131.45 35.80 2.44 51.52 3.41 42.63 3.30 1.30 11.70 67.60 10.90 [6]
Jingpo-* 1496 132.65 36.56 2.48 50.07 2.96 44.49 2.64 1.10 12.94 69.18 9.76
Jino Jino---1 Yunnan 22.0 100.8 120 120 240 122.01 35.74 2.96 54.04 2.50 40.50 3.13 0.83 10.00 80.00 17.50 [11]
Jino---2 Yunnan 22.0 100.8 395 439 834 123.82 36.42 3.43 55.75 2.22 38.60 1.74 0.42 6.54 78.06 15.05 [6]
Jino---* 1074 123.42 36.27 3.33 55.37 2.28 39.02 2.05 0.51 7.31 78.49 15.60
Kazak Kazak Xinjiang 43.8 87.6 500 500 1000 134.11 37.86 2.61 52.52 4.23 40.64 9.20 2.55 30.40 61.75 34.70 [6]
Kirgiz Kirgiz Xinjiang 39.5 76.0 500 500 1000 139.47 38.88 2.81 49.10 3.77 44.32 9.55 1.95 25.25 63.35 31.70 [6]
Korean Korean-1 Jilin 42.9 129.5 200 200 400 142.75 36.10 3.00 49.43 8.17 39.40 7.75 1.85 6.70 41.65 16.70 [20]
Korean-2 Jilin 42.9 129.5 205 277 482 136.13 36.00 1.21 48.90 2.40 47.49 7.67 1.77 6.49 41.73 16.94 [16]
Korean-3 Inner Mongolia 46.0 122.0 270 267 537 136.74 37.42 2.32 51.66 2.82 43.20 4.57 0.84 11.82 73.65 17.97 [14]
Korean-4 Liaoning 41.6 123.4 300 300 600 102.22 30.62 3.08 51.50 2.48 42.94 2.00 0.83 13.58 56.33 8.25 [16]
Korean-* 2019 127.53 34.80 2.42 50.51 3.68 43.39 5.18 1.26 10.06 54.54 14.58
Lahu Lahu---1 Yunnan 21.9 101.4 91 87 178 141.34 35.04 1.12 57.47 2.75 38.66 5.90 1.12 8.71 80.34 19.66 [11]
Lahu---2 Yunnan 22.5 99.9 90 110 200 153.32 36.08 2.10 34.55 1.20 62.15 3.00 0.25 15.00 61.25 8.00 [21]
Lahu---3 Yunnan 21.9 101.4 268 300 568 148.61 34.92 0.95 41.90 1.69 55.46 4.94 1.06 18.35 70.90 7.14 [16]
Lahu---4 Yunnan 22.5 99.9 480 500 980 143.16 35.85 1.26 45.67 1.94 51.13 2.70 0.82 29.49 64.49 4.85 [19]
Lahu---* 1926 145.65 35.52 1.24 44.49 1.87 52.40 3.69 0.86 22.78 67.51 7.22
Lhoba Lhoba Tibet 29.2 94.1 142 190 332 147.05 38.40 1.47 41.72 1.54 55.27 8.58 0.15 12.95 82.53 14.31 [6]
Li Li-----1 Hainan 18.6 109.7 258 270 528 133.76 36.48 2.66 48.84 2.20 46.30 2.48 0.79 5.24 42.63 12.81 [16]
Li-----2 Hainan 19.9 109.6 406 152 558 142.88 37.08 2.87 46.04 2.90 48.19 5.29 2.96 19.00 72.67 16.22 [16]
Li-----* 1086 138.45 36.79 2.77 47.40 2.56 47.27 3.92 1.90 12.31 58.06 14.56
Lisu Lisu---1 Yunnan 24.3 97.9 110 95 205 144.41 38.26 1.56 41.90 1.22 55.32 1.46 0.00 6.10 60.98 1.95 [11]
Lisu---2 Yunnan 25.9 98.7 500 283 783 137.56 38.33 1.98 49.95 3.83 44.24 2.17 0.57 10.92 73.95 7.92 [6]
Lisu---* 988 138.98 38.32 1.89 48.28 3.29 46.54 2.02 0.45 9.92 71.26 6.68
Man Man Liaoning 40.6 120.6 242 230 472 126.03 33.18 2.01 49.06 2.78 46.15 6.78 0.85 8.37 51.80 16.63 [16]
Maonan Maonan Guangxi 24.8 108.2 240 240 480 130.63 36.31 3.46 52.83 2.42 41.29 3.75 2.71 13.75 67.92 14.90 [11]
Miao Miao---1 Hainan 18.6 109.7 181 150 331 140.12 37.15 1.99 53.44 1.81 42.76 4.25 1.21 11.65 57.74 10.33 [16]
Miao---2 Sichuan 28.1 105.7 188 167 355 131.86 38.69 4.00 60.88 2.90 32.22 1.42 1.96 13.92 59.81 11.65 [11]
Miao---3 Guizhou 26.6 108.0 221 182 403 133.05 38.94 1.49 44.89 2.16 51.46 3.44 1.49 11.43 74.08 8.35 [16]
Miao---* 1089 134.81 38.31 2.46 52.70 2.30 42.54 3.03 1.56 12.31 64.46 10.03
Monba Monba Tibet 27.9 91.9 101 116 217 157.91 39.46 1.07 39.20 1.80 57.93 7.14 0.00 17.05 72.81 25.58 [6]
Mongol Mongol-1 Inner Mongolia 42.2 118.9 300 300 600 123.70 32.37 2.53 46.30 2.47 48.70 2.33 1.42 15.67 58.92 14.25 [16]
Mongol-2 Yunnan 24.0 102.7 313 413 726 133.40 40.05 2.39 55.89 1.83 39.89 5.51 0.69 14.12 71.07 7.02 [19]
Mongol-3 Inner Mongolia 46.0 122.0 515 553 1068 143.34 35.97 1.84 45.53 2.83 49.80 7.51 2.33 24.47 67.01 15.02 [14]
Mongol-* 2394 135.40 36.31 2.18 48.86 2.44 46.52 5.61 1.60 19.13 66.21 12.40
Mulam Mulam--1 Guangxi 24.7 108.9 226 261 487 126.41 36.99 4.33 48.52 2.57 44.58 7.91 1.54 16.22 85.12 14.68 [16]
Mulam--2 Guangxi 24.7 108.9 260 260 520 135.25 36.93 2.67 51.06 1.87 44.40 6.73 1.45 15.96 72.50 13.56 [11]
Mulam--* 1007 130.97 36.96 3.47 49.83 2.21 44.49 7.30 1.49 16.09 78.60 14.10
Naxi Naxi---1 Yunnan 26.8 100.2 310 310 620 132.02 36.99 1.89 46.52 2.16 49.43 2.26 0.97 16.05 81.54 13.55 [16]
Naxi---2 Yunnan 26.8 100.2 408 420 828 132.21 37.77 1.10 43.40 2.14 53.36 5.26 0.91 19.44 70.53 12.34 [19]
Naxi---* 1448 132.13 37.44 1.44 44.73 2.15 51.68 3.98 0.94 17.99 75.24 12.86
Nu Nu-----1 Yunnan 26.4 99.2 73 65 138 132.50 36.93 1.74 45.79 1.82 50.65 6.16 0.36 9.05 91.31 10.14 [11]
Nu-----2 Yunnan 25.9 98.7 175 176 351 149.03 39.08 1.34 45.89 2.71 50.06 6.41 0.43 16.81 73.79 8.40 [6]
Nu-----* 489 144.37 38.47 1.45 45.86 2.46 50.23 6.34 0.41 14.62 78.73 8.89
Oroqen Oroqen Inner Mongolia 51.7 126.6 184 238 422 146.34 35.83 2.41 45.86 2.19 49.54 10.65 1.01 10.91 25.20 18.36 [16]
Primi Primi Yunnan 26.4 99.2 159 138 297 157.84 39.27 1.65 38.08 1.42 58.85 12.96 1.35 14.14 86.53 8.59 [11]
Qiang Qiang--1 Sichuan 31.6 103.8 262 149 411 145.97 39.29 1.66 43.78 2.80 51.76 7.79 0.89 7.54 64.55 9.94 [16]
Qiang--2 Sichuan 31.6 103.8 296 272 568 164.32 40.14 2.10 48.34 2.68 46.88 10.77 1.49 18.74 63.57 11.56 [16]
Qiang--* 979 156.62 39.78 1.91 46.43 2.73 48.93 9.52 1.24 14.04 63.98 10.88
Russ Russ Xinjiang 43.8 87.6 31 25 56 143.87 38.45 3.93 56.97 3.39 35.71 7.14 1.79 25.89 54.46 15.18 [22]
Salar Salar Qinghai 35.8 102.4 102 102 204 149.40 40.21 1.72 44.85 4.95 48.48 8.58 1.72 19.36 75.98 25.49 [23]
She She Zhejiang 28.5 119.9 270 155 425 134.20 37.21 3.70 49.36 2.68 44.26 11.31 1.50 15.20 70.70 13.20 [16]
Sui Sui----1 Guizhou 26.0 107.8 135 170 305 145.40 36.32 1.79 43.32 2.05 52.84 7.33 2.61 16.13 77.03 16.45 [16]
Sui----2 Guizhou 26.0 107.8 206 207 413 136.60 37.07 1.77 41.55 1.91 54.77 2.54 1.57 11.02 72.28 13.44 [16]
Sui----* 718 140.34 36.75 1.78 42.30 1.97 53.95 4.57 2.01 13.19 74.30 14.72
Tajik Tajik Xinjiang 37.7 75.2 562 500 1062 134.26 39.00 6.57 47.49 2.65 43.29 4.24 3.30 28.25 50.75 26.93 [16]
Tatar Tatar Xinjiang 43.8 87.6 29 24 53 146.58 41.35 2.64 59.62 4.91 32.83 4.72 2.83 39.62 59.43 41.51 [24]
Tibetan T.B.---1 India 28.0 77.0 156 150 306 148.10 39.82 1.48 41.98 2.08 54.46 4.18 0.49 9.11 63.92 18.59 [16]
T.B.---2 Tibet 29.6 91.1 182 189 371 145.95 39.30 1.20 38.13 1.45 59.22 4.75 0.55 4.07 50.81 16.96 [16]
T.B.---3 Sichuan 33.0 101.7 223 181 404 148.03 39.72 1.88 41.68 2.00 54.44 7.35 0.00 18.25 75.18 14.70 [16]
T.B.---4 Sichuan 32.4 104.4 246 242 488 153.56 37.12 1.97 42.25 2.52 53.26 13.42 0.71 10.66 66.19 18.65 [16]
T.B.---5 Tibet 29.6 91.1 226 291 517 142.31 39.11 1.97 41.45 1.72 54.86 6.93 1.07 9.49 72.60 17.91 [25]
T.B.---6 Sichuan 31.8 102.4 341 326 667 161.49 39.79 1.87 47.45 3.60 47.08 9.98 0.70 14.26 72.40 10.06 [11]
T.B.---7 Gansu 34.9 102.9 500 500 1000 168.10 34.95 3.04 44.71 3.00 49.25 11.20 1.60 6.00 63.95 12.30 [16]
T.B.---8 Tibet 29.6 91.1 500 500 1000 143.62 38.01 1.18 41.74 2.73 54.35 6.10 0.60 11.70 82.00 25.90 [6]
T.B.---* 4753 153.00 38.01 1.92 42.92 2.57 52.59 8.44 0.82 10.31 70.03 17.08
Tu Tu Qinghai 36.8 101.9 106 108 214 143.47 39.66 1.92 50.98 2.90 44.20 7.95 1.64 19.16 73.36 21.96 [26]
Tujia Tujia Sichuan 28.4 108.9 265 240 505 120.04 38.54 2.43 45.84 1.86 49.87 8.51 1.48 12.97 60.79 16.43 [11]
Uygur Uygur Xinjiang 43.8 87.6 500 500 1000 138.09 37.27 2.51 50.28 3.75 43.46 14.90 4.70 39.15 62.00 33.10 [6]
Uzbek Uzbek Xinjiang 46.8 82.8 600 600 1200 152.00 38.00 3.46 49.39 2.76 44.39 5.91 5.63 45.67 54.38 27.00 [16]
Va Va-----1 Yunnan 22.7 99.4 416 354 770 137.78 37.63 2.01 56.36 2.09 39.54 2.80 0.58 16.28 77.56 9.75 [19]
Va-----2 Yunnan 23.1 99.2 500 400 900 139.60 38.20 2.34 57.61 2.82 37.23 2.67 1.06 14.39 73.67 13.67 [16]
Va----* 1670 138.76 37.94 2.19 57.03 2.48 38.30 2.73 0.84 15.26 75.46 11.86
Xibe Xibe Xinjiang 43.7 81.5 500 500 1000 146.50 39.00 1.81 45.39 2.63 50.17 7.50 1.80 21.05 64.95 21.00 [16]
Yao Yao----1 Guangxi 24.9 107.7 350 140 490 123.14 35.69 2.51 51.63 1.96 43.90 12.45 0.92 20.71 52.25 7.55 [27]
Yao----2 Guangxi 24.1 107.2 376 168 544 128.45 34.00 3.20 43.58 2.41 50.81 1.47 2.48 13.79 65.07 7.54 [16]
Yao----* 1034 125.93 34.80 2.87 47.39 2.20 47.54 6.67 1.74 17.07 58.99 7.54
Yi Yi-----1 Sichuan 28.0 102.8 180 160 340 150.63 40.38 2.12 52.50 2.76 42.62 6.18 1.18 14.27 79.27 16.91 [11]
Yi-----2 Yunnan 25.0 102.7 200 200 400 139.15 39.42 1.33 52.50 2.47 43.70 2.25 0.25 16.13 79.00 13.38 [11]
Yi-----3 Yunnan 25.0 101.5 250 250 500 135.08 37.80 1.10 43.60 1.52 53.78 4.00 0.00 12.80 67.20 17.80 [16]
Yi-----4 Sichuan 27.7 102.8 434 71 505 153.48 41.34 2.00 46.37 3.09 48.54 5.73 1.16 7.73 48.79 11.50 [16]
Yi-----5 Yunnan 24.7 103.2 500 500 1000 135.38 38.90 1.62 51.20 2.82 44.36 2.00 0.20 16.15 66.60 9.50 [6]
Yi-----* 2745 141.09 39.41 1.62 49.28 2.57 46.53 3.60 0.47 13.75 66.81 12.86
Yugur Yugur Gansu 38.8 99.6 185 151 336 147.40 40.71 2.03 44.30 2.29 51.38 9.05 1.63 18.99 55.79 25.07 [16]
Zhuang Zhuang-1 Guangxi 23.8 106.6 298 202 500 133.40 37.79 3.98 48.20 2.00 45.82 5.50 2.60 25.00 75.60 14.30 [16]
Zhuang-2 Guangxi 23.1 107.1 287 283 570 129.55 36.27 2.75 52.09 2.63 42.53 5.30 1.70 15.10 68.50 18.50 [11]
Zhuang-* 1070 131.35 36.98 3.32 50.27 2.34 44.07 5.39 2.12 19.73 71.82 16.54
Mang Mang Yunnan 22.7 103.2 124 110 234 118.42 36.71 4.10 62.44 2.35 31.11 6.41 0.00 8.98 64.10 6.62 [16]
Gin Gin-VieT Vietnam 21.0 106.0 66 69 135 128.00 36.30 5.40 46.90 1.70 46.00 0.40 1.10 12.30 65.20 9.30 [30]
Africans Africans South Africa 200 200 400 124.72 37.62 4.85 64.70 2.70 27.75 1.13 9.40 41.75 83.50 34.13 [28], [29]
Caucasians Caucasia USA 200 200 400 131.65 41.35 7.95 61.45 4.40 26.20 7.10 2.50 37.65 45.85 35.20 [3]
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*

A “*” indicates a combined sample for an ethnic group. Among the 56 ethnic groups, 31 are represented by more than one sample and 25 by only one sample. English names of these ethnic groups were based on Chinese Encyclopedia - Ethnic Groups (Encyclopedia Publisher, Beijing, Shanghai, Jun. 1986), and these English names were arranged in alphabetical order. The sample sizes for Bouyei, Dong-1, Dong-2, Ewenki, Han-5, Miao-2, Miao-3, Oroqen and Yugur are those from fingerprints since sample sizes for each dermatoglyphic variable may not be the same in a population. There are 1509 fingers in 151 Yugur females with one of them having an injured middle finger of the right hand. The following data are published for the first time: II of GaoS.-1, II and H of GaoS.-2, III of Han-1, II and III of Han-2, IV and H of Salar, T/I of Tatar, and II and III of Tu. Data II of Han-13 was kindly provided by Dr. Hui Li. The frequency of II of Russ from females was originally reported incorrectly as 20%. A total of 34 samples were investigated by authors of this paper, and these samples are Achang-2, Bai-2, Blang-2, Dai-2, De'ang-2, Derung-2, GaoS.-1-2, Han-1-2-10-15, Hani-2-3, Hui-7, Jingpo-2, Jino-2, Kazak, Kirgiz, Lahu-4, Lhoba, Lisu-2, Monba, Mongol-2, Naxi-2, Nu-2, Russ, Salar, Tatar, T.B.-8, Tu, Uygur, Va-1, and Yi-5. Among the 56 ethnic groups, we studied 29 (51.79%). Among the 121 populations, we studied 34 (28.10%).

Table 2. Principal Component analysis of 29 PM & 2 SM and Han-10 of Shanghai.

No. PMa & SMb ethnic groups PCI zi1 PCII zi2 PCIII zi3 PCIV zi4
1 PM-Sc Achang-2 0.2399 −0.1747 0.6042 −0.4677
2 PM-S Bai----2 −0.0529 −0.4894 0.5706 −0.2354
3 PM-S Blang--2 −0.1020 −1.3572 −0.4173 1.4570
4 PM-Nd Bonan--2 −0.2290 0.2155 0.5376 −0.1416
5 PM-S Dai----2 0.4040 −0.7465 −0.1697 −0.9512
6 PM-N Daur 0.0015 0.2013 0.1736 −0.1806
7 PM-S De'ang-2 0.2649 −0.6173 −0.2867 −1.2678
8 PM-S Dong---2 0.0086 −0.3368 0.2246 −0.0644
9 PM-N Dongxiang −0.0867 0.3428 −0.5854 −0.5603
10 PM-N Ewenki −0.3760 0.2720 −1.9678 0.1914
11 PM-S Hani---3 0.0682 0.0177 0.6914 −0.1180
12 PM-N Hezhen −0.1786 0.2268 −1.7250 0.9234
13 PM-N Hui----4 −0.4375 0.5904 −0.2046 −0.6614
14 PM-S Jingpo-2 0.1461 −0.8400 −0.2555 −0.7281
15 PM-S Jino---2 0.2725 −1.1702 0.1397 −0.0252
16 PM-N Korean-2 −0.2645 −0.2273 −1.5184 0.4181
17 PM-N Lhoba −0.6095 0.0769 1.1188 0.7162
18 PM-S Lisu---2 0.0533 −0.6645 0.7987 −1.8339
19 PM-S Maonan 0.2757 −0.5810 −0.3408 0.5417
20 PM-N Monba −0.6430 0.8287 1.5115 0.6402
21 PM-N Mongol-3 −0.1168 0.1244 −0.0349 0.6394
22 PM-S Mulam--2 −0.0156 −0.3476 −0.0309 0.7808
23 PM-N Oroqen −0.4078 0.4133 −2.4687 0.3475
24 PM-N Qiang--2 −0.2862 0.8945 0.2502 −0.5886
25 PM-N Titeban-8 −0.3730 0.2263 1.4243 0.1645
26 PM-N Xibe −0.1612 0.6358 0.5821 0.2102
27 PM-S Yi-----5 0.0145 −0.5395 0.7167 −0.9459
28 PM-N Yugur −0.2458 1.1025 0.4154 0.1799
29 PM-S Zhuang-2 0.1841 −0.4111 −0.2357 0.3549
30 SM Africans 1.5700 0.4936 0.8020 2.9551
31 SM Caucasians 1.3888 1.6194 −0.8029 −1.8662
32 ? e Han----10 −0.3063 0.2212 0.4827 0.1162
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a

PM - population marker.

b

SM - supervisory marker.

c

N - northern population.

d

S - southern population.

e

? - simulated population remaining to be determined.

Results

Results from the Cluster Analysis of 56 Chinese Ethnic Groups

Figure 1 shows the results of a cluster analysis performed on the 156 samples. These samples include 122 population samples, 31 combined samples, and Africans, Caucasian Americans and Gin Vietnamese. The cluster analysis shows two major sub-clusters: a southern group (1–71) and a northern group (72–154), demonstrating that all ethnic groups in China do not share similar physical characters.

Figure 1. Cluster tree for 156 samples (including 56 Chinese ethnic groups, Africans, Caucasians and Gin Vietnamese).

Figure 1

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There are 156 populations numbered from top to bottom 1–156. In the figure, there is a southern group (SG) (1–71) and a northern group (NG) (72–154). There are two outgroups: Caucasians (155) and Africans (156). Gin-Vietnamese (70) clusters with the SG. The cluster tree was drawn using the average linkage method.

Southern group (1–71)

This group contains 70 Chinese samples (excluding Gin-Vietnamese), and includes only nine samples from northern China. Fifty six percent of these northern samples (5 samples) are concentrated in clusters 57–66. Geographically, this can be treated as an area of transition between the southern group and the northern group, or as a mixed area. For the physical characters of dermatoglyphics, there is a process of gradual diffusion from south to north or from north to south. Migration and mixing of many ethnic groups are still restricted by geographical barriers.

Northern group (72–154)

This group contains 83 samples. Clusters 115–126 contain samples from southern China. Therefore, this cluster could be seen as a transition area between the northern group and the southern group. In the northern group, there are several ethnic groups from Xinjiang province (Kazak, Kirgiz, Uygur, Uzbek, Tatar, Tajik) and Salar of Qinghai Province. These seven samples constitute a cluster by themselves. With the exception of the Salar, the fingerprint frequency of whorl (W) among these six Xinjiang samples is significantly lower than the frequency of loop (L) (p<0.01), and the frequency of true pattern in the third interdigital area (III) in the hands is higher than 20%. Our Xingjiang samples express clear characters similar to the peoples of Central and Western Asia, and they could be treated singly as a “northwest group”.

Some Experience

Africans and Caucasian Americans, working as outgroups, express clear and suitable positions on the cluster tree. Gin-Vietnamese cluster in the southern Chinese group. Caucasian Americans first cluster with the Tajik and then cluster with northwest samples. Africans form the most peripheral cluster.

Combined samples representing 31 ethnic groups are included in the cluster analysis. The frequencies of their dermatoglyphic variables were calculated using the population size of each population sample. The combined samples tend to show a general picture for a specific ethnic group.

Sichuan is a province with many minorities of large population size in southwestern China. Ten (Han-8, Miao-2, Qiang-1-2-*, Tibetan-6-3-4, Tujia, Yi-4) of 11 samples from this province cluster in the northern group (including the Qiang combined sample) with only one sample (Yi-1) clustering in the southern group. During the past three centuries, the population in Sichuan has increased from 100 thousand to 100 million. Most likely, Sichuan is a place of migration and fusion of peoples.

Han Chinese are represented by 16 samples (including combined samples): 4 samples (Han-4-6-11-9) cluster in the southern group and 12 samples (Han-8-10-1-2-14-7-12-*-15-13-5-3) cluster in the northern group. Han-2 and Han-14 are neighboring samples in the northern group on the cluster tree, but Han-2 and Han-14 were collected separately from the south and north. Two samples (Han-6-9) were collected from the north but cluster in the southern group. Nine samples (Han-8-10-1-2-7-12-15-13-5) that were collected from the south actually cluster in the northern group. Three samples of Han Chinese in Shanghai (Han-10-15-13), with each sample having more than 1000 persons, all cluster in the northern group. Within clusters 109–114, there is a section containing many samples of Han Chinese. Samples of Han Chinese do not cluster into a single group. Han Chinese is the ethnic group with the largest population in China and throughout the world. Cluster analysis indicates that Han Chinese samples from different places (east, northwest, northeast and southwest) tend to cluster together as a group with local minorities. Therefore, the dermatoglyphic characters of Han Chinese express strong nationwide diversities.

Many large migrations through history, including migrations from south to north and from north to south, as well as migrations relating to the opening of the Silk Road for interchange between the east and the west, have divided the original ethnic groups into different populations. For example, migratory populations such as Mongol-2 and Hui-2-7-3 who migrated from northern China to southern China cluster with a neighboring ethnic group (southern group). This indicates correlations between physical characters of dermatoglyphics and geographical areas. Clearly, there can be large differences between migratory populations and the original population within the same ethnic group.

All nine Tibetan samples (including combined samples) cluster with the northern group although they are geographically located in southwestern China. There are five Tibetan samples (Tibetan-*-3-Ind.-5-8) in cluster 85-98 where Tibetan populations are relatively concentrated. Tibetan dermatoglyphics shows characters of the northern group. Therefore, it seems that Tibetans are a northern group and not a “southern group from India” as has been suggested by scholars. It seems likely that Tibetans originated from the ancient Qiang people in northern China.

Tibetan-4 is a population whose origin is up for debate, and they are known as Baima Tibetan people in Sichuan province. On the cluster tree, Tibetan-4 clusters with Gansu Tibetan (T.B.-7) in northwestern China. This suggests that there is a difference between Baima Tibetan People and Tibetan people living in Tibet. Tibetan migrants in India (T.B.-1) cluster with the Tibetan sample from the Lhasa area (T.B.-5), expressing a close relationship between these two populations.

Mang is a population that has not yet been assigned to an ethnic group. In the cluster tree, Mang clusters with Miao-2 and Russ (138,139). This result does not help to assign them to a particular ethnic group.

Regarding the Miao samples, Miao-1 was collected from Hainan Island (province) and clusters in the southern group, and Miao-3-2 in Sichuan and Guizhou provinces cluster in the northern group. This result may be explained by evolution of physical characters occurring in populations that are isolated in an island setting.

Minnan Han Chinese (Han-2) is the largest population in Taiwan. Their dermatoglyphics are similar to the mainland northern group [10]. Minnan people in Taiwan come from the southern part of Fujian Province, and Minnan people in Fujian originate from northern China. The dermatoglyphics of Hakka Han Chinese in Taiwan (Han-1) are also similar to the northern group [9].

Taiwan aborigines (Gaoshan ethnic group) are represented by two samples in this research: Amis (GaoS.-2) [8] with a large population (167 thousand) and the Kavalan sample (GaoS.-1) [7] with a very small population (about 800). Taiwanese aboriginal samples (GaoS.-1-2-*) all cluster in the northern group. It is not clear why they do not share a close relationship with southern Chinese minorities.

Yi people in Yunnan Province are represented by two samples in the analysis: Samei (Yi-2) and Luoluobo (Yi-3). They separately cluster in the southern group and the northern group. Differences between these Yi populations are obvious.

Six samples, including Bai-2, Yi-5, Jino-2, Hani-2-3, and Blang-2 studied by Haiguo Zhang and colleagues, and five different samples collected from the same ethnic groups (Bai-1,Yi-2, Jino-1, Hani-1, Blang-1) studied by Anlu Jin and colleagues, all cluster in the southern group. Also, Derung-2 [6] studied by Haiguo Zhang and colleagues and Derung-1 [11] studied by Anlu Jin and colleagues both cluster in the northern group. Scholars from different research teams can obtain similar results using different samples collected from the same ethnic groups in Yunnan province. This fact demonstrates that the technical analysis [1]–[3] standard and variables standard [6], [19], [31] required by the Chinese Dermatoglyphics Association (CDA) has great value and effectiveness.

Discussion

Dermatoglyphic characteristics can divide Chinese populations into a southern group and a northern group, taking the Yangtze River or 300–330 latitude as the boundary. This conjecture is similar to the results of dermatoglyphic research conducted in 1998 [6]. Previous studies from anthropometrics, HLA and immunoglobulin have also suggested that Chinese ethnic groups can be divided into northern and southern groups, and that they may be of different origins. [16]. Since there are great differences between the southern and northern groups, it is better to use data collected from local ethnic groups as references for medical applications and genetic studies.

There has been much debate about the origins of many Chinese populations and about proper assignment of these peoples to ethnic groups. Dermatoglyphic data can inform these debates by helping to classify a population as a northern or southern group. In order to make such assignments, we selected 29 samples from the dataset as reference populations (as population marker, PM). The 29 reference populations were limited to northern ethnic groups that actually cluster into the northern group, and southern ethnic groups that actually cluster into the southern group. In addition, preference was given to populations with larger sample sizes. Two outgroups, Africans and Caucasian Americans (as supervisory marker, SM), were also used to make such assignments.

There are 11 clustering methods available for cluster analysis in SAS software. If a clustering method is suitable for assigning a population to the northern or southern group, it should output 29 reference populations and 2 outgroups divided into four groups in the cluster tree: a southern group, a northern group, an African group and a Caucasian group. After selection, we found five usable clustering methods: Average linkage, complete or longest distance method, flexible-beta method, McQuitty's similarity method, and Ward's minimum-variance method. All these methods can classify 31 samples into 4 large groups. Although each of these five methods results in a different position (Y axis) in the clustering figure or a different clustering distance (X axis) for each population, the positions of the populations within the four groups is relatively stable. Figure 2 is an example of the results for the average linkage method, from which the cluster figure for 31 samples and the Han Chinese in Shanghai (Han-10) has been drawn. The results from the cluster analysis show that the Han-10 sample should be assigned to the northern group.

Figure 2. Cluster tree for Shanghai Han (Han-10), 29 reference populations (PM) and 2 outgroups (SM).

Figure 2

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Shanghai Han clustered with the northern group. This figure was drawn based on cluster analysis using the average linkage method.

We also conducted principal component analysis on these 32 samples, and used PCI and PCII to make a scatter diagram (Figure 3). The Han Chinese in Shanghai (Han-10) were also assigned to the northern group in this analysis. Principal component analysis and cluster analysis produced identical results. Although Shanghai is south of the Yangtze River, these two analyses assign this city to the northern group. Not surprisingly, only 14% of individuals in the sample have both parents from Shanghai. Shanghai is a typical immigrant city.

Figure 3. Scatter gram for principal component analysis of Shanghai Han (Han-10), 29 reference populations (PM) and 2 outgroups (SM).

Figure 3

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This figure was drawn based on standardized principal component scores. Shanghai Han (indicated by the “?”) stays in the northern group.

According to the principal component analysis, the first four components can explain 83.51% of the variance (41.61%, 20.73%, 10.62% and 10.54%, for each component respectively). In a previous study of 38 loci (130 alleles, including blood groups, HLA, red cell enzymes, serum proteins etc.) in 33 Chinese ethnic groups (106 populations), principal component analysis showed that the first four components could only explain 65.8% of the variance (30.4%, 17.2%, 12.2% and 6.0%, for each component respectively) [16]. Thus, these dermatoglyphic data can explain 17.71% more of the variance than did the genetic markers. This research demonstrates that dermatoglyphics, although a classical discipline, still shows vitality and good future prospects.

The Mang are a population that have not been assigned to any of the 56 Chinese ethnic groups. Therefore, we conducted a cluster analysis to determine its most closely related group. Figure 4 shows a cluster tree that includes the 31 reference samples and the Mang. The results show that the Mang cluster with the Southern Group. We also conducted principal component analysis on the 32 samples, and used PCI and PCII to make a scatter diagram (Figure 5). The Mang are also assigned to the Southern Group in this analysis. This result fits with the fact that they currently reside in southern China.

Figure 4. Cluster tree for Mang, 29 reference populations (PM) and 2 outgroups (SM).

Figure 4

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Mang was in the southern group. This figure was drawn based on cluster analysis using the average linkage method.

Figure 5. Scatter gram for principal component analysis of Mang, 29 reference populations (PM) and 2 outgroups (SM).

Figure 5

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This figure was drawn based on standardized principal component scores. Mang (indicated by the “?”) stays in the Southern group.

Dermatoglyphic data, coupled with cluster analysis and principal component analysis, are a useful tool for assigning Chinese populations to the northern or southern group. Dermatoglyphic data from Chinese ethnic groups can also be used as reference populations or outgroups when doing anthropological research.

Materials and Methods

Some of the dermatoglyphic data used in this study were obtained from previously published articles or books. The authors of this paper studied 29 ethnic groups (33 samples) [6]–[10], [19], [22]–[24], [26], which account for 51.79% (29/56) of all ethnic groups. There are 6 ethnic groups with less than 10,000 people in China, and we completed research of 4 samples among them (Monba: 7500 people, Derung: 5800 people, Tatar: 5000 people, Lhoba: 2300 people). Parents of all investigated subjects are healthy and of the same ethnic group. Three samples are used as outgroups: Africans [28], [29], Caucasians [3], and Gin Vietnamese [30]. 121 samples from 56 ethnic groups in China as shown in Table 1 contain a total of 68,846 individuals with 35,950 males and 32,896 females (excluding Indian Tibetans (T.B.-1)) [16].

The standard of technical analysis for dermatoglyphics used for this research is called the Cummins' standard or the Euro-American standard [1]–[3], because it was strongly promoted by an American, H. Cummins, but was originally suggested by F. Galton (1822–1916) and E. R. Henry (1850–1931) from the U.K. [1]. The Chinese Dermatoglyphics Association (CDA) follows this Euro-American standard. According to CDA standards, 11 dermatoglyphic variables must be included in all research: total finger ridge count (TFRC), a–b ridge count (a–b RC), percentage frequencies of the arch (A), ulnar loop (Lu), radial loop (Lr) and whorl (W), percentage frequencies of true pattern in the thenar area (T/I), second interdigital area (II), third interdigital area (III), fourth interdigital area (IV) and hypothenar area (H).

SAS software was used to perform cluster analysis (see Figure S1 in Supporting Information File S1) and principal component analysis using a 156×11 data matrix. Through the computation of these two analyses, we created a cluster tree and scatter diagram using PCI and PCII (see Figure S2 in Supporting Information File S2). We also developed some computer programs for frequency calculating or weighting using QBASIC or C++.

Dermatoglyphic data from other research teams used in this paper has been carefully checked. The total frequency for several dermatoglyphic variables must add up to 100%. If the total did not reach 100%, this could have been caused by publication error or miscalculation, and needed to be corrected. No data were included in the research when there was no way to correct for such errors.

All dermatoglyphics were obtained by ink print. All our analyses on dermatoglyphics were based on these ink prints.

No data were included in the research when there was no way to correct for such errors.

Supporting Information

Supporting Information File S1

(0.09 MB DOC)

Click here for additional data file. (91KB, doc)
Supporting Information File S2

(0.61 MB DOC)

Click here for additional data file. (599KB, doc)

Acknowledgments

HGZ would like to thank all the workers who collected or analyzed dermatoglyphics of Chinese people. He would also like to express his appreciation for the assistance of Jia-Ming Liu and Lin-Lie Sun on computer programming for making figures. He thanks professor Qing-Po He for assistance with statistics.

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: HGZ thanks the Chinese National Human Genome Center at Shanghai for their grant support.

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