|Year : 2019 | Volume
| Issue : 1 | Page : 55-62
Quantitative anthropometric and dermatoglyphic variation of the major ethnic populations in Nigeria
Moses Olusola Adetona1, Mathew Temitayo Shokunbi2
1 Department of Anatomy, College of Medicine, University of Ibadan, Ibadan, Nigeria
2 Department of Anatomy; Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
|Date of Submission||06-Nov-2018|
|Date of Decision||05-May-2019|
|Date of Acceptance||25-Jun-2019|
|Date of Web Publication||28-Nov-2019|
Dr. Moses Olusola Adetona
Department of Anatomy, College of Medicine, University of Ibadan, Ibadan
Source of Support: None, Conflict of Interest: None
BACKGROUND: Anthropometry is one of the oldest and widely used measures of human variation. Dermatoglyphics is a valuable technique in human population studies by virtue of its uniqueness, genetic determination, and less vulnerability to selection than other genetic markers.
AIMS: The study aims (1) to elucidate the traditional ethnic identities in Nigeria which are increasingly facing disintegration due to improved means of communication and urbanization and reduced inbreeding and (2) to describe ethnic characteristics that may be valuable for forensic application and future studies of effects on human diversity.
SUBJECTS AND METHODS: We obtained quantitative anthropometric and dermatoglyphic data from 560 volunteers of both sexes, of Yoruba, Igbo, and Hausa origin. The sampling fraction used to attain target sample size for random selection of eligible volunteers was based on the national population figure.
STATISTICAL ANALYSIS: Univariate analysis of variance was used to determine patterns variations, while multivariate analysis was used to determine discrimination among ethnic populations.
RESULTS: The anthropometric and dermatoglyphic variables revealed a discrimination that is consistent with ethnohistorical affiliations. Multiple discriminant analysis of the anthropometrics showed higher discrimination power than the dermatoglyphic variables. The derived ethnic classifying equations from anthropometric parameters classified volunteers as Yoruba 78.2%, Hausa 82.4%, Igbo 91.4%; the dermatoglyphic parameters classified volunteers as Yoruba 66.8%, Hausa 57.4%, Igbo 65.3%. The canonical discriminant function of the anthropometric and dermatoglyphic variables showed clustering of the ethnic populations around each ethnic centroid.
CONCLUSIONS: The results provide ethnohistorical insights into the structure of the ethnic populations and demonstrate the relationship of the gene flow in the ethnic groups through their exhibited phenotypic characteristics.
Keywords: Anthropometry, dermatoglyphics, ethnic population differentiation
|How to cite this article:|
Adetona MO, Shokunbi MT. Quantitative anthropometric and dermatoglyphic variation of the major ethnic populations in Nigeria. J Exp Clin Anat 2019;18:55-62
|How to cite this URL:|
Adetona MO, Shokunbi MT. Quantitative anthropometric and dermatoglyphic variation of the major ethnic populations in Nigeria. J Exp Clin Anat [serial online] 2019 [cited 2021 May 8];18:55-62. Available from: https://www.jecajournal.org/text.asp?2019/18/1/55/271867
| Introduction|| |
Nigeria is a multilinguistic, multicultural, and pluralistic nation with each ethnic population making claims of oral, cultural, and historical identity. The physical characteristics of the different ethnic populations may be used for population subdivision and identity and may have forensic, public health, and industrial applications. Although the degree of ethnic isolation is not high and there has been a long period of historical interactions, each ethnic population has acquired well-defined historical, geographic, and cultural identity. Nevertheless, the traditional ethnic identities are at increased risk of disintegration due to improved means of communication and urbanization and reduced inbreeding. It is necessary to study their current characteristics to have insights into future trends of the identity of each ethnic population.
Previous anthropometric and dermatoglyphic studies have documented differences in phenotypic features among Nigerian ethnic populations (Hiernaux and Froment, 1976; Taiwo and Akinde, 2012; Danborno et al., 2009; Umar et al., 2011; Igbigbi et al., 1996; Adetona et al., 2008). Conventionally, the Maguzawa tribe (Hausa) inhabited Northwest Nigeria (Kallamu, 2013), the Igbo traditional habitation covers most of Southeast Nigeria (Slattery, 2016), while the Yoruba ethnic population is found mainly in Southwest Nigeria (Facts.ng, 2016). These three ethnic population groups had inhabited the Niger area with a long history of migrations and settlements and with such admixture of social and cultural relationships that it often became difficult to separate the people within these settlements into neat sociocultural groups (Otitie, 2016). The quantifiable cephalometric differences of ethnic populations residing in Nigeria are small as observed from studies of several workers in different ethnic populations. It has been established that the differences in traits of the phenotypic characteristics among or between ethnic groups within a geographical area that had interrelated for a long time were usually very small with an enormous degree of overlap (Richardson, 1980). The factors accounting for these phenotypic variations are genetic, environmental, socioeconomic, and nutritional (Campbell and Tishkoff, 2008; Roberts, 1953, 1978; Hiernaux et al., 1975). The phenotypic variability in the expression of genes among individuals and ethnic populations influences the anthropometric and dermatoglyphic variables, which reveal differential history of human origin, and the complex interaction of genetic and environmental factors in producing phenotypes (Campbell and Tishkoff, 2008). Anthropometric and dermatoglyphic variables could therefore reveal patterns of the structure of population groups and their ethnohistorical affiliations (Knight et al., 2005).
This study was designed to quantify the physical characteristics of the three Nigerian ethnic groups and to determine the comparative power of anthropological tools for identification and other applications.
| Subjects and Methods|| |
Sample size determination was based on the development of nonlinear regression model for the estimation of genetic diversity of large natural population from finite sample sizes regardless of the species and marker systems (Bashalkhanov et al., 2009).
The Nigerian population (Np) figure was 140,431,790 (National Population Commission. 2010. Federal Republic of Nigeria 2006 Population and Housing Census. Priority Table IV). The Hausa population of northwest zone (Hp) was 35,915,467. The sampling fraction Hp/Np equaled to 0.3, resulting in the selection of three out of 10 eligible Hausa volunteers. The Igbo population of southeast zone (Ip) was 16,395,555. The sampling fraction Igbo population of southeast zone (Ip)/Np equaled to 0.1, resulting in the selection of one out of 10 eligible Igbo volunteers. The Yoruba population of southwest (Yp) was 27,722,452, and the sampling fraction Yp/Np equaled to 0.2, resulting in the selection of two volunteers out of every eligible 10 Yoruba volunteers. The target sample size was to attain a minimum of 90 volunteers for each ethnic population (Bashalkhanov et al., 2009). The total sample size of 560 was taken based on volunteers' availability: 175 for Hausa, 163 for Igbo, 222 for Yoruba.
Anthropometric measurements were obtained from voluntary participants using internationally accepted human anthropometric landmarks [Figure 1] and standard anthropometric procedures. Body weight in kilograms was measured by a Gallenkamp self-zeroing digital weight scale (DT 150 Weiss-Gallenkamp, Company, United Kingdom) accurate to 0.1 kg.
The standing height was measured using a stadiometer (Weiss-Gallenkamp, United Kingdom). The following parameters were measured using digital spreading calipers (Gujarat, India): head length (HH), maximum biparietal diameter (BPD), head (skull) height, nasal height (NH), upper facial height (UFH), lower facial height (LFH), bizygomatic distance (BZD) (facial width).
The cephalometric indices were calculated using standard equations: cephalic index, vertical index, height length index, head modulus index, index of the size of head, morphological facial index, morphological upper facial index, and sagittal nasofacial index [Appendix 1] (Singh et al., 2004). The following parameters were measured by a tape measure and a ruler on the right side of the volunteers (Hall et al., 2007): sitting height, head circumference (HC), right (R) and left (L) total upper limb length, upper arm length, forearm length, hand length (HAL), middle finger length, palm length (PL), palm width (PW), upper leg (thigh) length, lower leg (calf) length, foot length and foot width. Carrying angle (CA) in degrees was measured by a goniometer (White Plains, USA). All measurements were taken to 0.1 unit. The following parameters were calculated: cormic index (COI), ponderal index (PI), and body mass index (BMI) [Appendix 1].
The volunteer that had anthropometric measurements also had prints of the right and left hands taken with printer's ink on an A4 glossy paper. Each paper has the age, sex, right or left hand, and identification number preceded with Hausa (H), Igbo (I), and Yoruba (Y) on the top of the paper. The volunteer whole hand was placed on the inked slab. The ink was spread to areas of the hand that did not make contact with the slab from distal end of the fingers to the level of proximal wrist crease. The thumb and fingers 2, 3, 4, and 5 were rolled from the radial side to the ulnar to obtain their complete pattern [Figure 2].
|Figure 2: Finger and palmar patterns of the right hand showing finger patterns, palm areas a, b, c, d palm ridge density-1, 2, 3, 4, a-b ridges and atd angle|
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Readings of the print were done with × 4 Power Bifocal Margin Lamp Magnifier. The readings were based on the human hand print classification: the five digits (1–5), the thenar area (T), the hypothenar (H), and the central area of the palm (Ashbaugh, 1999). The print variables were classified using the internationally accepted Euro-American classification of palmar and digital prints into arch (A), ulnar loop (UL), radial loop (RL), and whorl (W). The ridges in a pattern were counted on a straight line connecting the core of the pattern and the triradius. The triradius point and the point of core were not included in the count. The following finger and palmar variables were obtained: total finger ridge count (TFRC), a–b RC, percentage frequencies of each arch (A), UL, RL, whorl (W) of both hands, and atd angle of both hands (atd [R], atd [L]). The palm print of right (R) and left (L) of the predefined areas: palm ridge density (PRD)-1 is defined as midpoint of line on thenar eminence connecting mid first metacarpophalangeal crease and mid-distal wrist crease; PRD-2 is a point on hypothenar eminence midpoint of straight line connecting mid of fifth metacarpophalangeal crease and mid distal wrist crease; PRD-3 is a points half centimeter proximal to triradius “a;” PRD-4 is a point half centimeter proximal to triradius “d.” A 25 mm 2 area was drawn on the defined areas (Acree, 1999) to estimate the RC. The ridges in 25 mm 2 area were counted to reflect the ridge density count. The variables for both hands were counted for individual within each ethnic population.
The study was approved by the Research Ethics Committee of the Ministry of Health (Reference number AD 13/620), and only participants who gave their informed consent were involved in the study.
Univariate analysis of variance (ANOVA) was used to study patterns of anthropometric and dermatoglyphic variations. Multivariate analysis of multiple discriminant analysis was used to examine different set of data for the ethnic populations.
| Results|| |
The results for anthropometric and dermatoglyphic univariate ANOVA are presented in [Table 1]. The F values of the anthropometric variables showed differentiation that is statistically significant (P < 0.05) for the ethnic groups for each of the variables, except PI, HC, CA (L), PL (R), PW (R), and PW (L). Similarly, the F values for the dermatoglyphic variables such as TFRC (L), a-bRC (L), atd (L), and all palm ridge densities for right and left (R and L) hand were significantly different (P < 0.05) between the ethnic groups. The results of multivariate analysis of the anthropometric and dermatoglyphic variables are presented in [Table 2]. It suggests significant discrimination (P < 0.05) for anthropometric and dermatoglyphic variables. The palmar dermatoglyphic variables are much more discriminatory when compared to finger dermatoglyphics.
|Table 1: Univariate analysis of variance for the anthropometric and dermatoglyphic among ethnic groups|
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|Table 2: Multivariate test statistic, Wilks' λ and P value for the extent of differentiation based on set of anthropometric and dermatoglyphic variables|
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[Table 3] shows the standardized discriminant function coefficients reflecting the contribution of the variates in the anthropometry and dermatoglyphics predicting the subjects to be in each of the ethnic group. The structure matrix of the variables pooled within groups discriminating variables and standardized canonical discriminant functions. The results showed clustering of the ethnic groups around each ethnic group centroid (a point representing an average location of all people in an area). The clustering of anthropometric and dermatoglyphic variables are shown in [Figure 3] and [Figure 4]. The anthropometric variables showed clustering of the ethnic variables around each of the ethnic centroid when compared with the scattering of the ethnic variables in the dermatoglyphic variables in graphical representation [Figure 3] and [Figure 4].
|Table 3: Classification function coefficients of anthropometric and dermatoglyphic variables among the three ethnic groups|
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|Figure 3: Anthropometric variables' canonical discriminant function showing clustering of the ethnic populations around individual ethnic centroid|
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|Figure 4: Dermatoglyphic variables canonical discriminant function showing clustering of the ethnic populations around separate centroid|
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The discriminant power of the anthropometric and dermatoglyphic variables in classifying the membership of ethnic populations is shown in [Table 4]. The anthropometric variables classify Yoruba subject as a member of Yoruba ethnic group in 78.2%, Yoruba as member of Hausa ethnic group in 9.5%, and Yoruba as member of Igbo ethnic group in 12.3%. It classifies Hausas subject as member of Hausa ethnic group in 82.4%, Hausa as member of Yoruba ethnic group in 5.9%, and Hausa as a member of Igbo ethnic group in 11.8%. It classifies Igbo subjects as member of Igbo ethnic group in 91.4%, as member of Yoruba ethnic group in 4.3%, and as a member of Hausa ethnic group in 4.3%. The dermatoglyphics classifies Yoruba subject as Yoruba in 66.8%, as member of Hausa ethnic group 18.8%, and as member of Igbo ethnic group in 14.4%. It classifies Hausa subject as member of Hausa ethnic group in 57.4%, as member of Yoruba ethnic group in 23.7%, and as member of Igbo ethnic group in 18.9%. It classifies Igbo subjects as member of Igbo ethnic group in 65.3%, as member of Yoruba in 18.0%, and as member of Igbo in 16.7%.
|Table 4: Percentage classification of ethnic classifying equation by multiple discriminant analysis of anthropometric and dermatoglyphics variables|
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| Discussion|| |
The population that shares genetic background and environmental factors will have the mean height that is a characteristic of the group. In this study, weight and stature differences of the three ethnic populations with each ethnic group having different shared genetic inheritance are the determinant factors of ethnic population classification. Human quantitative traits of weight and stature are heritable (Maes et al., 1997; Turula et al., 1990; Perola et al., 2007; McEvoy and Visscher, 2009; Tishkoff et al., 2009; Wood, et al., 2014, Marouli et al., 2017). The proportion of the total variation in height due to genetic factors is controlled by multiple genes and environmental factors (Silventoinen et al., 2003). The heritability of the difference in stature had been shown not to be part of the growth hormone/insulin-like growth factor 1 pathway (Tishkoff et al., 2009). The BMI, COI, and PI for the three ethnic populations are factors of height and weight and contributed to the determinant of the classification. Parental HC is predictive of offspring HC (Taiwo and Adeleye, 2013). Variation had also been shown to exist in cephalometry among major geographic groupings of Homo sapiens (Relethford, 1994). This study's anthropometric variables suggest significant differentiation of the three ethnic groups as shown by the multiple discriminant analysis. The significant classification differences exhibited in anthropometry could only be explained by the effect of genetic, dietary, and environmental factors (Roberts and Williamson, 2002). Most of these population groups had come to southwest on business trips and those that are residing in the southwest still adhere to the dietary habits known for that ethnic population. Significant effect of genetic, cultural, and environmental factors on somatometric and craniofacial variability had also been reported for adult of other ethnic populations (Varrela 1990; Buretic-Tomljanovica et al., 2007; Sandip et al., 2014; Akram et al., 2014; Brooke and Larsen, 2014). Numan et al. used anthropometric values of HAL in the three major ethnic groups in Nigeria for stature estimation; they showed that variations were present not only between races but also among ethnic groups. Anthropometric dimensions obtained from subjects of different birth places and regions also showed significant differences (Golalipour 2006; Du et al., 2008; Reddy et al. 2001). Farkas et al. have shown that the great similarities between the North American Whites and the European Caucasians, together with the stable characteristics maintained by Asians and Africans throughout their ethnic populations, can be explained only by considerable influence of inherited genetic factors. This study has shown that anthropometric variates can provide a level of insight into ethnic classification and identification and it can also be a useful tool in portraying ethnohistorical relationships.
Differences in the frequencies of various fingerprint patterns of distinct ethnic groups are well established (Cummins and Midlo, 1961; Maricq, 1972) and dermatoglyphic variables had been used for ethnic groups' geographic patterning (Zhang, 2010). The dermatoglyphics in this study classified subjects into the ethnic groups. Dermatoglyphic variables' differentiation of the three ethnic populations was not as discrete as that of anthropometric variables. It had been reported that dermatoglyphic variables undergo slow rates of evolutionary change and may not depict differences within ethnic level where local level variations were not submerged or where there was no major geographic and ethnic difference (Sachs and Bat-Miriam, 1957; Rothhammer et al., 1977; Rudan, 1978; Jantz et al., 1982; Reddy and Reddy 1992; Zhang et al., 2010). There is low genetic distance and identity among the three ethnic population groups which could be explained by the three groups' inhabiting a region with a history of long migrations and settlements with such a mixture of social and cultural relationships. It is difficult to separate these ethnic population groups within these settlements into neat sociocultural groups. Consistency of genetic and linguistic evolution had been known to be broken by factors such as less isolation, language replacement, or intermarriage. (Sun et al., 2013). The anthropometric parameters showed better defined discriminatory power than the dermatoglyphic parameters [Figure 3], [Figure 4] and [Table 4].
Most ethnic population groups were usually defined based on linguistic and physical characters and geographic birth location of sampled individuals, but Cavalli-Sforza et al. had reported that phenotypic characters can constitute a basis for learning about ethnic population evolutionary relationships. This study showed that Nigerian ethnic population groups have anthropometric and dermatoglyphic parameters that are similar due to some shared genetic factors; however, there are still significant quantifiable anthropological parameters that could differentiate them as reflected in the ethnic classification of membership. The anthropometric parameters [Table 3] showed distribution of quantitative values around each ethnic population centroid; it showed higher discrete clustering around the ethnic centroid compared to the distribution of dermatoglyphic quantitative values around each ethnic population centroid. The combined plotting of the three ethnic populations' [Figure 3] and [Figure 4] anthropometric and dermatoglyphic parameters showed the extent of ethnic populations' gene flow and admixture among the ethnic populations. The predictive accuracy [Table 4] of anthropometric ethnic classifying equation from multiple discriminant analysis suggests that gene flow from Yoruba to the other two ethnic populations exceeded that of Hausa and Igbo, while Igbo is still the most endogamous of the three ethnic population groups. The less classifying power of dermatoglyphic parameters was consistent with the finding of other workers (Sachs and Bat-Miriam, 1957; Rothhammer et al., 1977; Hawkinson, 1979; Reddy and Reddy, 1992).
| Conclusion|| |
This work provides insights into the structure of the ethnic populations, and demonstrate the relationship of the gene flow among Nigeria major ethnic groups through their phenotypic characteristics.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Acree M.A. (1999). Is there a gender difference in fingerprint ridge density? Forensic Sci Int 102 (1):35-44.
Adetona M.O., Oladapo O.O., Igbigbi P.S. (2008). Palmar and digital dermatogylyphic patterns of the three major ethnic groups in Nigeria. Afr J Med Med Sci 37 (4):333-7.
Akram N.A., Sheikh B.A., Ahmed M., Abbas A., Malik W.R., Azmi S. (2014). Assessment of genetic variation across a heterogeneous population using anthropometric data. Proc 12th
Int Conf Stat Sci 26:201.
Ashbaugh D.R. (1999). Quatitave-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. Taylor and Francis, Washington, D.C.
Bashalkhanov S., Pandey M., Rajora O.P. (2009). A simple method for estimating genetic diversity in large populations from finite sample sizes. BMC Genet 10 (1):84.
Brooke J.L., Larsen C.S. (2014). The nurture of nature: Genetics, epigenetics, and environment in human biohistory. Am Hist Rev 119 (5):1500-13.
Buretic-Tomljanovica A., Giacomettib J., Ostojica S., Kapovica M. (2007). Sex-specific differences of craniofacial traits in Croatia: The impact of environment in a small geographic area. Ann Hum Biol 34 (3):296-314.
Campbell M.C., Tishkoff S.A. (2008). African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu Rev Genomics Hum Genet 9:403.
Cavalli-Sforza L.L., Menozzi P., Piazza A. (1994). The History and Geography of Human Genes. 1st
ed. Princeton University Press, Princeton.
Cummins H., Midlo C. (1961). Finger Prints, Palms and Soles: An Introduction to Dermatoglyphics. Vol. 319. Dover Publications, New York.
Danborno B., Adebisi S.S., Adelaiye A.B., Ojo S.A. (2009). Estimation of height and weight from the lengths of second and fourth digits in Nigerians. Internet J Forensic Sci 3 (2):1-6.
Du L., Zhuang Z., Guan H., Xing J., Tang X., Wang L., et al
. (2008). Head and face anthropometric survey of Chinese workers. Ann Occup Hyg 52 (8):773-82.
Farkas L.G., Katic M.J., Forrest C.R. (2005). International anthropometric study of facial morphology in various ethnic groups/races. J Craniofac Surg 16 (4):615-46.
Golalipour M.J. (2006). The variation of head shapes in 17-20 years old native Fars male in Gorgan-North of Iran. Int J Morphol 24 (2):187-90.
Hall J.G., Judith A., Karen G., Anne S. (2007). Handbook of physical measurements. 2nd Ed. Canada. Oxford University Press. 84-238.
Hiernaux J., Froment A. (1976). The correlations between anthropobiological and climatic variables in sub-Saharan Africa: Revised estimates. Hum Biol 48 (4):757-67.
Hiernaux J., Rudan P., Brambati A. (1975). Climate and the weight/height relationship in sub-Saharan Africa. Ann Hum Biol 2 (1):3-11.
Igbigbi, P. S., Didia, B. C., Owhojedo, H., & Obochi, O. (1996). Comparative palmar and digital dermatoglyphics of Hausa and Urhobo ethnic groups in Nigeria. West Afr. J. Anat (4): 51-56.
Jantz, R. L., Hawkinson, C. H. (1979). Finger ridge-count variability in sub-Saharan Africa. Annals of human biology, 6(1): 41-53.
Jantz R.L., Hawkinson C.H., Brehme H., Hitzeroth H.W. (1982). Finger ridge count variation among various sub-Saharan African groups. Am J Phys Anthropol 57 (3):311-21.
Knight B, Shields B.M., Turner M., Powell R.J., Yajnik C.S., Hattersley A.T. (2005). Evidence of genetic regulation of fetal longitudinal growth. Early Hum Dev 81 (10):823-31.
Maes H.H., Neale M.C., Eaves L.J. (1997). Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 27 (4):325-51.
Maricq H.R. (1972). 'Ethnic'differences in the fingerprint data in an 'All White' control sample. Hum Heredity 22 (5-6):573-7.
Marouli E., Graff M., Medina-Gomez C., Lo K.S., Wood A.R., Kjaer T.R., et al
. (2017). Rare and low-frequency coding variants alter human adult height. Nature 542 (7640):186-90.
McEvoy B.P., Visscher P.M. (2009). Genetics of human height. Econ Hum Biol 7 (3):294-306.
Numan A.I., Idris M.O., Zirahei J.V., Amaza D.S., Dalori M.B. (2013). Prediction of stature from hand anthropometry: A comparative study in the three major ethnic groups in Nigeria. Br J Med Med Res 3 (4):1062.
Perola M., Sammalisto S., Hiekkalinna T., Martin N.G., Visscher P.M., Montgomery G.W., et al
. (2007). Combined genome scans for body stature in 6,602 European twins: Evidence for common Caucasian loci. PLoS Genet 3:e97.
Reddy B.M., Reddy P.C. (1992). Dermatoglyphic affinities among the Telugu populations with contrasting ethnohistorical backgrounds. Am J Hum Biol 4 (5):669-82.
Reddy B.M., Pfeffer A., Crawford M.H., Langstieh B.T. (2001). Population substructure and patterns of quantitative variation among the Gollas of Southern Andhra Pradesh, India. Hum Biol 73 (2):291-306.
Relethford J.H. (1994). Craniometric variation among modern human populations. Am J Phys Anthropol 95 (1):53-62.
Richardson E.R. (1980). Racial differences in dimensional traits of the human face. Angle Orthod 50 (4):301-11.
Roberts D.F. (1953). Body weight, race and climate. Am J Phys Anthropol 11 (4):533-58.
Roberts DF (1978) Climate and Human Variability, 2nd Ed. Menlo Park, CA: Cummings.
Rothhammer F., Chakraborty R., Llop E. (1977). A collation of gene and dermatoglyphic diversity at various levels of population differentiation. Am J Phys Anthropol 46 (1):51-9.
Rudan P. (1978). Biological structure of Istrian population – Study of distances by anthropometric traits, dermatoglyphic properties and blood group gene frequency. Collegium Anthropol 2:53-8.
Sachs L., Bat-Miriam M. (1957). Finger print patterns in Jewish populations in Israel. Am J Hum Genet 9 (2):117-26.
Sandip S., Sarun K., Chandra-Bhushan J. (2014). Effect of ethnicity on head for anthropometry of 17-26 year old normal population in Eastern Nepal. Eur J Anat 18 (3):135-9.
Silventoinen K., Sammalisto S., Perola M., Boomsma D.I., Cornes B.K., Davis C., et al
. (2003). Heritability of adult body height: A comparative study of twin cohorts in eight countries. Twin Res Hum Genet 6 (5):399-408.
Singh I.P., Bhasin M.K. (2004). A Manual of Biological Anthropology. Kamla-Raj Enterprises, Delhi.
Sun H., Zhou C., Huang X., Liu S., Lin K., Yu L., et al
. (2013). Correlation between the linguistic affinity and genetic diversity of Chinese ethnic groups. J Hum Genet 58 (10):686-93.
Taiwo, I. A., Adeleye, A. (2013). Parental determinants of offspring head circumference using a sample of patients attending a government hospital in Lagos, Nigeria. Nigerian quarterly journal of hospital medicine, 23(4): 287-294.
Taiwo I.A., Akinde O.R. (2012). Predictability of offspring birth weight using simple parental anthropometrics in a government hospital in Lagos, Nigeria. Int J Med Biomed Res 1 (3):206-14.
Tishkoff S.A., Reed F.A., Friedlaender F.R., Ehret C., Ranciaro A., Froment A., et al
. (2009). The genetic structure and history of Africans and African Americans. Science 324 (5930):1035-44.
Turula M., Kaprio J., Rissanen A., Koskenvuo M. (1990). Body weight in the Finnish twin cohort. Diabetes Res Clin Pract 10:S33-6.
Umar M.B., Ojo A.S., Asala S.A., Hambolu J.O. (2011). Comparison of cephalometric indices between the Hausa and Yoruba ethnic groups of Nigeria. Res J Med Sci 5 (2):83-9.
Varrela J. (1990). Effects of attritive diet on craniofacial morphology: A cephalometric analysis of a Finnish skull sample. Eur J Orthod 12:219-23.
Wood A.R., Esko T., Yang J., Vedantam S., Pers T.H., Gustafsson S., et al
. (2014). Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46:1173-86.
Zhang H.G., Chen Y.F., Ding M., Jin L., Case D.T., Jiao Y.P., et al
. (2010). Dermatoglyphics from all Chinese ethnic groups reveal geographic patterning. PLoS One 5 (1):e8783.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]
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