Tracing Shina and Wakhi Origins: Are Ethnic Classifications Commonly Used in Demographic Studies Biologically Meaningful?

Tracing Shina and Wakhi Origins: Are Ethnic Classifications Commonly used in Demographic Studies Biologically Meaningful?

Patrick O’Neill

Anthropology Program, School of Social Sciences and Education

California State University, Bakersfield

A Thesis submitted to the Anthropology Program, School of Social Sciences and Education

California State University, Bakersfield

In Partial Fullfillment for the Degree

Masters of Art

Winter 2013 2

Patrick W. O’Neill

2012

Tracing Shina and Wakhi Origins: Are Ethnic Classifications Commonly used in Demographic Studies Biologically Meaningful?

PW O’Neill Anthropology Program, School of Social Sciences and Education California State University, Bakersfield

Abstract The primary goal of this research is to determine whether highland Pakistani populations share close biological affinities to one another, or if they represent phenetically distant groups. This research is undertaken in order to: 1. Test models of dental inheritance and patterns of sex dimorphism based on tooth size; 2. Assess the reliability of linguistic, archaeological, and historically based classification of ethnic groups commonly employed in demographic studies; and 3. Test four current models for South Asian population history with tooth-size allocation analysis. It is important to consider phenetic affinities based on tooth size to develop a more complete understanding of biological relationships and general patterns of microevolution among living Pakistani populations. Additionally, understanding patterns of phenetic affinities between groups in these regions allows important questions posed by linguists, archaeologists, geneticists and historians to be addressed scientifically. This study is significant because very little work has been accomplished in Pakistan by bioanthropologists and human biologists. Further, teeth provide a unique opportunity to compare living and archaeological populations, so that population affinities and possible patterns of migration may be traced both geographically and temporally. Phenetic affinities between populations are assessed via tooth-size allocation analysis. The samples that form the basis of this study include 14 archaeologically derived samples from Central Asia, the Indus Valley and west-central peninsular India, as well as samples of 18 living groups from northern Pakistan and peninsular India. This thesis introduces samples from two previously uninvestigated ethno-linguistic groups, the Wakhi (n=326) and the Shin (n = 280)of Gilgit-Baltistan, northern Pakistan. Maximum mesiodistal and buccolingual measurements were obtained for all permanent teeth except third molars in accordance with standardized methods. Individual measurements were scaled against the geometric mean to control for sex dimorphism and evolutionary tooth size reduction. Inter-sample differences in tooth size allocation is assessed with pairwise squared Euclidian distances and the patterning of phenetic affinities among samples is assessed with hierarchical cluster analysis, neighbor-joining cluster analysis, multidimensional scaling, and principal coordinates analysis. The inability to identify consistent aggregates encompassing Pakistani highland ethnic groups suggests that significant population movements into this region have occurred over the last 1000 years, a phenomenon that has intensified, according to many historical sources, over the past 300 years (see for example Dani 2006). On the other hand, consistent identification of close phenetic affinities among Shina and Wakhi samples from Gilgit-Baltistan suggest that populations in this region are potentially of indigenous origin, but it must be remembered that the boundaries between Central Asia and northern South Asia are blurred, especially from a biological perspective. Overall, it appears that Gilgit-Baltistanis and highland populations from Chitral do not share common origins, nor is it likely that a “Dardic” biological ethnicity can be accurately applied to all northern Pakistani ethnic groups that speak Indo-Aryan languages. This may be a result of marriages between proximal groups such as the Wakhi and the Shina who must seek marital partners outside of their villages in order to avoid incest taboos. Finally, and perhaps most importantly, is the fact that neither the small dialectical differences nor the occupation of different geographic locations between the two Shina groups considered in this study appears to have had any effect upon their biological affinities. Therefore, it appears that ethnic classifications based on linguistic familiarity have biological meaning, and are appropriate and meaningful when used properly in demographic studies.

Table of Contents List of Figures……………………………………………………………………………………………… 6 List of Tables………………………………………………………………………………….….………... 7 Acknowledgments…………………………………………………………………………….………….. 8 Chapter One: Introduction…………………………………………………………………………….. 9 Statement of the Problem…………………………………………………………………….. 9 Determining Phenetic Affinities via Biodistance Analysis……………………………… 10 Why Allocation of Permanent Tooth Size?...... 11 South Asian Population History: Why Pakistan?...... 12 Chapter Two: Justification of Tooth Size as an Indicator of Phenetic Affinities between Ethno-linguistic Groups……………………………………………………..…….. 13 Odontogenesis……………………………………………………………………………………. 13 Heritability of Dental Crown Size……………………………………………………………. 21 Use of Twin Studies to Assess Overall Genetic Control of Tooth Size……………….. 22 Evidence for Sex-linked Inheritance of Crown Diameters…………………………………………………………………………………………………… 25 Evidence of Heritability Demonstrated by Family and Sibling Studies……………… 26 Chapter Three: Ethno-historic Background………………………………………………………… 33 The Wakhi………………………………………………………………………………………… 33 The Shin…………………………………………………………………………………………… 41 Chapter Four: Previous Anthropometric, Anthroposcopic, and Genetic Research ………...49 Anthropometry and Anthroposcopy………………………………………………………………….. 49 Risley (1915)……………………………………………………………………………………… 49 Giufrida-Ruggeri (1917)………………………….………………………………………….... 54 Haddon (1924, 1929)…………………………………………………………………………… 55 Von-Eikstedt (1934)…………………………………………………………………………….. 58 Hutton (1932)…………………………………………………………………………………….. 60 Guha (1935, et al. 1951, 1955)………………………………………………………………. 64 Uniparental Molecular Genetic Markers…………………………………………………… 65 Chapter Five: Materials and Methods……………………………………………………………...... 73 Observer Error…………………………………………………………………………………………….. 75 Sex Dimorphism…………………………………………………………………………………………… 80 Asymmetry………………………………………………………………………………………………..... 86 Comparative Samples……………………………………………………………………………………. 88 Living South Indians……………………………………………………………………………………… 88 Living Northwest Indians………………………………………………………………………………... 94 Prehistoric Indus Valley…………………………………………………………………………………. 96 Prehistoric Central Asia…………………………………………………………………………………. 99 Living Pakistani Highlanders…………………………………………………………………………… 100 Chapter Six: Models and Expectations………………………………………………………………. 101 The Aryan Invasion Model………………………………………………………………………………. 103 The Long-Standing Continuity Model………………………………………………………………… 105 The Early Entrance Model………………………………………………………………………………. 107 The Historic Era Interactions Model………………………………………………………………….. 110 Expectations……………………………………………………………………………………………….. 112 Chapter Seven: Chapter Seven: Previous Odontometric Studies Conducted by Members of the Centre for South Asian Dental Research……………………………………….. 115 Chapter Eight: Results of Current Study……………………………………………………………. 134 Discussion………………………….………………………………………………………………………. 138 References………………………………………………………………………………………………….. 144 Appendix A: Descriptive Statistics...………………………………………………………………….. 168 Appendix B: Geometric Means…………………………………………………………………………. 171 Appendix C: Diagonal Matrix of simple Euclidean distances………..…………………………. 172 5

List of Figures

Figure 1: Racial Types of India according to Risley (1915), slightly modified from the original text ………………………………………………..………………………………………………. 51 Figure 2: Risley’s (1915) examples of the seven Indian racial types.……...………………….. 53 Figure 3: Eickstedt’s (1952a) racial types of India..…………………………………………...... 59 Figure 4: Guha’s Racial Types …………………………………….………………………………...... 63 Figure 5: Bar Graph showing levels of intraobserver error……………………………………… 77 Figure 6: Bar graph showing levels of interobserver error …………………………………...... 79 Figure 7: Bar Graph Showing Levels of Sex Dimorphism among Wakhi Samples by Tooth and Dimension.……………………………………………….…………………………………… 82 Figure 8: Bar Graph Showing Percentage of Sex Dimorphism among Shina Samples by Tooth and Dimension…………………………………………………………………………..……. 84 Figure 9: Bar Graph Showing Percentage of Sex Dimorphism among Shina and Wakhi Samples by Tooth and Dimension..…………….………..………………………….………. 85 Figure 10: Locations of Collection of Odontometric Samples .………………….……………… 88 Figure 11: The Aryan Invasion Model………………………………………………………………… 104 Figure 12: The Long-Standing Continuity Model………………………………………………….. 106 Figure 13: The Early Entrance Model………………………………………………………………… 107 Figure 14: Renfrew’s (1987) hypotheses.……………………………………………………………. 108 Figure 15: The Historic Era Interactions Model……………………………………………………. 111 Figure 16: Preliminary results of Cluster Analysis (Hemphill et al. 2012), presented at the 2007 AAPA meetings (Hemphill et al. 2007)………………………………………………... 117 Figure 17: Preliminary results of Principal Coordinates Analysis (Hemphill et al. 2012), presented at the 2007 AAPA meetings (Hemphill et al. 2007)………………………………….. 118 Figure 18: Results of Heirarchical Cluster Analysis (Hemphill 2009)………………………… 118 Figure 19: Results of Neighbor-joining Cluster Analysis (Hemphill 2009)…………………… 119 Figure 20: Results of Multidimensional Scaling (Hemphill 2009)……………………………… 119 Figure 21: Results of Heirarchical Cluster Analysis (O’Neill and Hemphill 2009)…………. 121 Figure 22: Results of Neighbor-joining Cluster Analysis (O’Neill and Hemphill 2009)……. 121 Figure 23: Results of Multidimensional Scaling (O’Neill and Hemphill 2009)………………. 122 Figure 24: Results of Heirarchical Cluster Analysis (Willis 2010)…………………………...... 123 Figure 25: Results of Neighbor-joining Cluster Analysis (Willis 2010)……………………….. 124 Figure 26: Results of Multidimensional Scaling (Willis 2010)………………………………….. 124 Figure 27: Results of Heirarchical Cluster Analysis (Hemphill 2010)……………………...... 126 Figure 28: Results of Neighbor-joining Cluster Analysis (Hemphill 2010)………………….. 126 Figure 29: Results of Multidimensional Scaling (Hemphill 2010)…………………………….. 127 Figure 30: Results of Principal-coordinates Analysis (Hemphill 2010)………………………. 128 Figure 31: Results of Heirarchical Cluster Analysis (O’Neill and Hemphill 2010)………… 130 Figure 32: Results of Neighbor-joining Cluster Analysis (O’Neill and Hemphill 2010)...... 131 Figure 33: Results of Multidimensional Scaling (O’Neill and Hemphill 2010)…………...... 131 Figure 34: Results of Heirarchical Cluster Analysis with Complete Linkage………………. 135 Figure 35: Results of Neighbor-joining Cluster Analysis……………………………………….. 136 Figure 36: Results of Kruskal’s Multidimensional Scaling…………………………………….. 137 Figure 37: Results of Principal Coordinates Analysis…………………………………………… 138

List of Tables

Table 1: Abbreviations and Sample Sizes for Odontometric Data……………………………… 73 Table 2: Measurement and Statistical Assessment of Intra-observer Error via Paired-Samples T-tests with Bonnferoni and Dunn-Sidak adjustments…………………….. 76 Table 3: Measurement and Statistical Assessment of Inter-observer Error via Paired-Samples T-tests with Bonnferoni and Dunn-Sidak adjustments…………………….. 78 Table 4: Descriptive Data for Wakhi Samples Based on Sex……………………………………. 81-82 Table 5: Descriptive Data for Shina Samples Based on Sex………...………………………….. 83-84 Table 6: Results of paired t-tests for Side Differences among the Wakhi……………………. 86 Table 7: Results of paired t-tests for Side Differences among the Shina…………………….. 87

Aknowledgments This work would never have been possible without the support of my friends, fellow students, family, professors, and mentors. I will begin by extending my general gratitude to all of my friends and fellow students. Thank you all for being there when I needed you, for your interest in my well-being, and for all the good times we have had. Second, I want to thank my parents for encouraging me to give up a life of sex, drugs, and rock & roll for an education and a promising future. Thanks Mom and Dad, for everything. This work is dedicated to the two of you. I want to extend a special thanks to Dr. David Rosales, who is the first of my mentors in college, one of my most trusted friends, and an inspiration to my academic career. Dr. Rosales, your kind words of encouragement and sincerity as a teacher propelled me through my lower division classes and helped me to build the self-confidence and determination required to complete two bachelor’s degrees. Moreover, the time spent acting as you teacher’s aide gave me the experience that later helped me become an adjunct faculty member at Bakersfield College. Your belief in me will always be appreciated, and you will always be my friend. I will always remember you as the professor who inspired me to greatness in acedamia and directed me onto a path of intellectual achievement. Most of all, I want to thank the members of my thesis committee, who have spent endless hours reading and editing this work. I will begin with the outside member of my committee, Dr. Paul Smith. While I do not know you well, Dr. Smith, I greatly appreciate your willingness to participate as a member of my committee and thank you for all of the time and efforts you have invested in my thesis experience. Your expertise in human biology and population genetics adds candor to this work. The second member of my thesis committee is Dr. Robert M. Yohe, II. Dr. Yohe, you are my teacher and my friend. I want to thank you, not only for being a member of my thesis committee, but also for the experience I gained acting as a teacher’s aide and guest lecturer in your undergraduate classes. Additionally, it is to you I owe my employment at Bakersfield College, where I received my first assignment as an adjunct professor of anthropology during Fall 2010. This assignment was entirely due to your recommendation. I thank you for being my professor, for bringing good humor into the classroom, for helping me achieve my dream of teaching at the college level, and also for being a good friend. I look forward to the day we become collegues, whether it be in the halls of academia or in the field. Thanks again! Finally, with great humility and appreciation, I want to thank Dr. Brian E. Hemphill, who is the chair of my thesis committee, my professor, and my friend. Honestly, words are not enough to describe the sense of appreciation and gratitude I feel toward you. Nonetheless, I will do my best to describe my thoughts here. It is to you, above all others, that I owe my sense of personal integrity and professional confidence in the field of anthropology. This is because, not only have you led by example in these regards, as a teacher, a scientist, and as a community leader, you have also pushed me to be the best that I can be as a student, as a teacher, and as an anthropologist. This is because you set the highest standards for my performance and refused to settle for anything less. While at times the classes were difficult and the expectations seemed too high for me to conquer, I considered it a challenge and sought to complete what I started, if for no other reason, just to impress you with my abilities to percervere. You provided me, not only with the knowledge I needed to proceed to graduate school, but you also provided me with a project and the materials that made my research possible. Under your tutelage, I received four research grants and participated in four international academic conferences. Additionally, you allowed me to accompany you to India, a trip that marks my first experience abroad. To keep things brief I will only say that India truly opened my eyes and changed my perspectives and views in regard to nearly everything in life. In addition to being a great teacher and an inspiration to my academic creativity and integrity, you have become a true friend. You have been there for me when I needed you, on so many levels. I will always be your friend and thank you for everything that you have done for me that has resulted in my success as a student, as a teacher, and as a man. Once again, thank you to my friends, family, and committee members for helping to make this thesis a work of enduring legacy.

Chapter One: Introduction Statement of the Problem

The primary goal of this research is to determine whether highland Pakistani populations share close biological affinities to one another, or whether they represent phenetically distant groups. This is undertaken in an effort to: 1. Test models of dental inheritance and emanine patterns of sex dimorphism based on tooth size. 2. Assess the reliability of linguistic, archaeological, and historically based classification of ethnic groups commonly employed in demographic studies. 3. Test the four current models of South Asian population history with tooth-size allocation analysis. It is important to consider phenetic affinities based on tooth size to develop a more complete understanding of biological relationships and general patterns of microevolution among living Pakistani populations.

Additionally, understanding patterns of phenetic affinities between groups in these regions can help answer important questions posed by linguists, archaeologists, geneticists and historians.

This study is significant because very little work has been accomplished in Pakistan by bioanthropologists and human biologists. Further, teeth provide a unique opportunity to compare living and archaeological populations, so that population affinities and possibly patterns of migration may be traced both geographically and temporally. The general research questions for this study are:

1.) Does odontometric variation among living Karakoram Highland populations demonstrate patterns of biological affinities between ethnic groups according to language, status (class, caste), geographic proximity, or sex? That is, are historical, linguistic, and geographically-based ethnic classifications commonly used in demographic studies biologically meaningful?

2.) As the expression of sex dimorphism is known to be a genetically controlled phenomon that is subject to an environmental constraint known as “blunting” (Stini, 1969), does the lower-status Shin dentition exhibit less sex dimorphism in mesiodistal lengths and buccolingual breadths than found among their higher- status neighbors the Wakhis.

3.) Which of the models for the population history of South Asia are best supported by the patterning of biological affinities possessed by the living and prehistoric ethnic groups considered in this study?

This thesis is organized into two parts. The first provides a justification of the biological utility of tooth size allocation analysis for reconstruction of population histories. The second provides a case study that examines phenetic affinities among highland populations of northern Pakistan via tooth size allocation analysis that places them in greater geographic and temporal context by comparing them to an array of prehistoric and living ethnic groups from

Central and South Asia. This is undertaken to test the expectations of an array of models for the initial peopling and subsequent interactions among the myriad ethnic groups of South

Asia.

Determining Phenetic affinities via Biodistance Analysis

Biodistance analysis produces measures of the divergence between populations through assessment of variation in the expression of polygenic traits. Polygenic traits are influenced both genetically and environmentally. Hence, such traits can be used to measure variation between genetically and environmentally differentiated populations. Biological distance analysis was the predominant method of measuring relatedness between living and past populations prior to the advent of modern genetics research involving DNA extraction. Skeletal biodistance analysis is still the primary method for determining genetic relationships among archaeological samples found in regions where DNA cannot be obtained from ancient remains due to diagenic processes. Skeletal and dental biodistance studies have contributed much to anthropological understanding of regional population histories, but rarely are living samples compared with archaeological samples in this manner. This is because one encounters myriad problems when attempts are made to compare skeletal measurements performed on skeletons to anthropometric measurements performed on living individuals, largely because of the confounding influence of soft tissue upon the latter. In general, phenetic-distance analyses predict genetic relatedness between populations based on phenotypic traits. Hence, the degree of relatedness should be highest among populations that exhibit similar variation across phenotypic traits and lowest among populations that exhibit little or no similarities (Larsen

1997). It is important to mention here that biodistance analyses are based upon variables that

have little of no selective value. Hence, differences identified between populations are due solely to genetic drift, for they are free of conflation by selective pressures.

Why Allocation of Permanent Tooth Size?

The reasons a researcher ought to choose differential allocation of size across the permanent dentition as a method of studying genetic affinities between human populations in

Central and South Asia are easy to define. The most general benefit of employing such a method is related to the accessability of odontometric data. That is, collecting odontometric data from living individuals is easy and inexpensive. With little effort, the dentitions of volunteer subjects can be cast in dental stone and curated safely without a need for sophistocated facilities. While genetic analyses have been useful in determining biological affinities between living populations in Central and South Asia, the statistical methods employed in such studies produce coalescence estimates that encompass timespans ranging from 10,000 to 60,000 years (Hemphill 2012). Such error ranges make it difficult to consider the population history of the region with sufficient chronological specificity. Another problem is related to preservation issues for skeletal deposits found in these regions. Despite recent advances in the amplification of ancient DNA (aDNA), the extraction of such material has been almost completely unsuccessful thus far in South Asia. This is because much of this region is characterized by an osscilating water table that rises and falls frequently, a process that gradually removes the organic collagen from buried bone. An additional geological factor that limits preservation is the presence of acidic soils across the region, which causes demineralization of buried bone and skeletal elements.

A second problem with skeletal deposits at archaeological sites located in South Asia is related to the pressure of overburden, which often crushes cranial elements, making craniometric investigations impossible. Teeth, in light of these issues, stand as a way out of the

South Asian population history dilemma; for they are the only skeleto-dental element exposed in living individuals, and as such they serve as the only hard-tissue link between populations of the past and present that can be measured for the purpose of biological comparison. Thus,

because tooth size is under strong genetic control (Garn et al. 1968), and because tooth size is known to covary with other genetically controlled biological traits (Hemphill 1991), assessment of tooth size allocation throughout the permenant dentition is an appropriate method for the identification of biological relatedness between regionally and chronologically defined populations in South Asia and adjacent regions.

South Asian Population History: Why Pakistan?

Populations living today in the northwestern borderlands of the Indian subcontinent provide a unique opportunity to assess population affinities between ethnic groups of Central and South Asia. This is particularly true of ethnic groups that inhabit the rugged highlands of northern Pakistan. Because bioanthropological research is all but missing from this region, and because Pakistan stands between Central Asia and peninsular India, an assessment of biological affinities between these groups is pivotal for understanding genetic relationships and what these relationships mean in terms of the historical and microevolutionary processes related to the gene flow that accompanies migration as well as the genetic drift caused by reproductive isolation among human populations. Another advantage to studying highland

Pakistani populations from a biological perspective is that these populations live in close proximity to both the Oxus and the Indus Valleys. As such, an understanding of biological affinities among ethno-linguistic groups of northern Pakistan can shed light upon unanswered questions in linguistics, history, and archaeology, surrounding the possible contacts and the nature of those contacts between the prehistoric, protohistoric, and historic populations of these two regions.

Chapter Two: Justification of tooth size as an indicator of phenetic affinities Between ethno-linguistic groups

Odontogenesis

Humans, as mammals, possess teeth that are formed from oral epithelial and mesenchymal tissue during fetal development. The epithelium is host to Hertwig’s epithelial root sheath as well as enamel-producing ameloblasts, while the mesenchym produces odontoblasts responsible for dentin formation, cementum, alveolar bone and the dental pulp.

Lumsden (1988) demonstrated that basal ectomesenchymal tissue involved in dento-facial development has its origin in the neural crest of the occipital bone. Mesenchyme cells originate in the middle and hindbrain regions, and proliferate across the branchial arches and facial region prior to the onset of dental development (Neiminen 2007).

The process of tooth development encompasses four phases; 1) initiation; 2) morphogenesis; 3) differentiation; and 4) eruption. Just before the initiation stage, development of the medial nasal processes, the processes of the branchial arches for each jaw, and the dental arches are completed. This occurs after mesenchyme cells are deposited in layers along the conformation of the developing jaws during the sixth fetal week. The epithelium then begins to grow until it joins the alignment of mesenchyme cells and forms the primary epithelial band.

Beginning during the seventh week of fetal development, the primary epithelial band splits, producing the dental lamina and the vestibular band. Between the seventh and the tenth fetal weeks, epithelial placodes appear on the dental laminae along each jaw, later to take form as the enamel organs. Most researchers agree these events mark the beginning of the initiation stage of dental development (Tonge 1969, Pispa and Thesleff 2003, Neiminen 2007), for these initially formed enamel organs produce the enamel of the deciduous tooth crowns. Formation of permanent tooth buds begins approximately six weeks later, and initiates a long sequence of timing in development that is completed after birth (Scott and Simons 1982).

Cohn (1957) demonstrated that after formation of the placodes, underlying mesenchyme cells are condensed, followed by growth of the epithelium into thickened 13

mesenchyme tissue. This process forms an epithelial bud on the enamel organ thus indicating the bud stage of tooth germ development and the beginning of morphogenesis, the second phase of dental development. The tooth bud consists of an outer layer of condensing mesenchyme tissue containing the outer basal epithelial layer and the inner stellate reticulum

(Neimen 2007). After the initial bud forms, an enamel knot made of non-dividing cells forms at its tip and the cervical loops form from the surrounding epithelium, beginning the cap stage of tooth germ development (Jernvall et al.1994, 2000, Hillson 1996).

During the cap stage, a cavity, known as the papilla, forms on the side of the enamel organ. The mesenchyme tissue surrounding the papilla and the cervical loops form the dental follicle, while the cementum and dentine are generated from the inner mesenchyme.

Secondary enamel knots then begin to form on the epithelium, designating the locations of additional cusps. The differentiation stage of dental growth begins at the end of the cap stage of tooth germ development. The enamel organ differentiates the basal epithelium into two components, the inner and outer enamel epitheliae. The inner enamel epithelium is oriented toward the papilla, and eventually forms the enamel matrix, while the outer enamel epithelium faces the dental follicle. The stratum intermedium spans the distance between the inner epithelium and the stellate reticulum as the enamel organ grows into the bell stage of tooth development. During the bell stage, the cavity within the papilla deepens and develops the pattern of folds that later define tooth crown shape (Scott and Symons 1982, Hillson 1996,

Neiminen 2007). Consequently, the bell stage is best understood to be a brief period of complex histodifferentiation and morphodifferentiation prior to morphogenesis.

The next phases of dental development, morphogenesis and differentiation, are intrinsically related processes. The differentiation process separates out the columnar cells responsible for hard tissue formation, which begins at the apices of the secondary enamel knots. Meanwhile, mesenchymal cells of the internal enamel epithelium differentiate and mature into elongate, polarized odontoblasts that begin to deposit dentin matrix at the apices of the enamel knots. At the same time, ameloblasts are formed from papillary cells and begin to

secrete enamel matrix in layers upon the matrix of predentin. As the predentin matrix becomes thicker, odontoblastic activity is paused and the dental tubules are formed. This initiates mineralization of predentin, which eventually matures into dentin. The differentiation process ends with the mineralization of the enamel out of organic mesenchymal cells into non- dividing structural enamel cells (Ten Cate 1994, Hillson 1996, Thesleff and Neiminen 2005,

Neiminen 2007).

The differentiation stage of tooth germ development determines the general shape and size of the dental crowns when hard tissue generating cells become disassociated with organic tissues. As enamel matures, it becomes thicker, thereby increasing the size of the dental crown prior to eruption. Dental morphogenesis is intrinsically tied to alveolar osteogenesis.

This process is followed by innervation of the tooth pulp, which must be vascularized in order to bring life into otherwise non-organic dental structures. As such, teeth become enclosed in pockets of alveolar bone allocated by dental follicular cells. Later, above (mandible) and below

(maxilla) the alveoles throughout dental ontogeny, the alveolar bone must be resorbed to permit tooth eruption. When the cemetoenamel junction is attained through ameloblastic activity, differentiation of hard tissue cells abates and the epithelium forms Hertwig’s root sheath, which grows into the mesenchyme tissue of the dental cavity. Simultaneously, the mesenchymal cells of the dental papilla are differentiated so that odontoblasts generate and deposit the dentin of the root. Dental follicular cells then produce cementoblasts that deposit cementum atop the dentin of the root (see Cohn 1957, Gaunt 1964, Scott and Symons 1982,

Ten Cate 1994, Hillson 1996, Luukko, 1997, Thesleff and Nieminen 2005, Nieminen 2007).

The secondary, or permanent dentition, arises from the same dental lamina of the enamel organ that produces the primary, or deciduous, teeth. The primary teeth are the predecessors of the secondary teeth. An exception to this rule is the premolars, which have no analogue among the deciduous dentition. The odontogenesis of a secondary tooth initiates by forming enamel organs while its primary antecedent is still in the bell stage of development

(Scott and Symons 1982, Luckett 1993, Ten Cate 1994). That odontogenesis, as a process of

in-utero development, is genetically determined is evident from the fact that teeth exhibit regular growth and eruption intervals. Examination of the development of teeth by class indicates genetically controlled timing of odontogenesis in humans and many other mammalian species (Butler 1937, Dalhberg 1945). This is demonstrated by the fact that swellings for the first incisors, canines, and first premolars appear simultaneously, but on different locations of the dental lamina. Second incisor swellings begin just after the appearance of the key premolar and anterior teeth (Dahlberg 1945, Luckett 1993).

The first molar is the first of the posterior permanent teeth to develop. This tooth is initiated simultaneously with the formation of the deciduous teeth, while the second and third molars appear later, originating post-natally from the dental lamina (Ten Cate 1994, Nieminen

2007). Deciduous teeth develop and mineralize rapidly relative to their permenant counterparts. The process of morphogenesis of the permanent teeth encompasses approximately 17 - 21 years of development post-utero. While deciduous teeth are almost completely mineralized at the time of birth, permanent teeth mineralize post-natally, beginning with the first molars by three years of age. The permanent first molars erupt between six and seven years of age. The remaining deciduous teeth are lost and replaced by permenant teeth erupting primarily between the ages of six and twelve years of age, with the exception of the third molars, which erupt between 17 – 21 years of age (Pirinen and Thesleff 1995, Hillson

1996).

Morphogenetic theories of tooth development have roots in Huxley and de Beer’s (1934) embryonic field theory. Huxley and de Beer identified morphogenetic fields on the sides of

Amblystoma insect embyros that appeared to control limb production. Because they could not identify specific areas of control for limb production, Huxley and de Beer (1934) asserted that limb formation could be controlled at any loci within the embryonic field. Huxley and deBeer

(1934) argued that all constituents of an embryonic field are controlled by the same heritable factors. However, recent research by Stern and Emlen (1999) recently identified factors of size control for dung beetle limbs that vary according to location in the embryonic field.

Butler (1937) argued, based on previous work by Bateson (1894) and his own examination of Zalambodonts, that teeth express a high degree of merism, and therefore develop as members of a unit, controlled by a region within the morphogenetic field, rather than as individual structures. This argument was based on observation of obvious similarities in shape and size between adjacent teeth in non-human mammals. In 1939, Butler elaborated upon his earlier work by demonstrating that tooth formation occurs along a mesiodistal gradient in which later developing members of a morphogenetic field develop distal to the initial tooth of each tooth class. Butler (1939) concluded that morphogenetic substances control dental development in different regions of the jaw. As such, within each district of the jaw, tooth rudiments form according to morphogenetic agents housed in each respective neighborhood of the oral community. That is, the rudiments themselves are shaped by the morphogenetic field, suggesting that the forces of control over tooth germ development originate externally.

According to Butler’s (1937, 1939) field hypothesis, three morphogenetic fields are present in the dentition of non-primate mammals and they are associated with the incisors, canines, and molars, respectivley. Further, since the highest amount of field morphogens exists in association with polar teeth (I1, C, M1: the first formed in each tooth class), these teeth are considered to be the most stable in size and shape. Consequently, stability of the size and shape characteristics among distal members of each morphogenetic field ought to be diminished. Butler (1939), in partial agreement with Bateson (1894), argued that tooth germs respond independently and in different ways to regional morphogenetic fields of the dental arcade. Theoretically, this could explain the variation in the shape of the premolars that lie adjacent to key teeth. That is, if the tooth bud is located next to the adjacent tooth class, such mesial and distal members may experience caninization or molarization, respectively (Keiser

1990). The problem with this assertion is the assumption that premolars do not have a morphogenetic field of their own. This assumption, if proven incorrect, would also overlook the

possibility that deciduous and permanent dental fields are expressed independently of one another.

Dahlberg (1945) applied Butler’s (1937, 1939) field hypothesis to the study of the human dentition. While Butler’s (1937, 1939) field hypothesis was useful in dental developmental research, defining “morphogenetic substances” in a physical sense proved to be a difficult, if not impossible task1. Nonetheless, Dahlberg (1945, 1986) identified four morphogenetic fields (tooth classes) in the human jaw (incisors, canines, premolars, molars).

Following Butler (1939), Dahlberg (1945) argued that the first tooth formed within each tooth class act as polar or “key” teeth in humans. Hence these teeth should be subject to high levels of morphogen exposure within their respective fields of the jaw and should exhibit less variability than adjacent teeth of the same morphogenetic field (distal teeth). In 1973, Butler elaborated further upon the field morphogen theory, arguing that batteries of genes drive dental development of undifferentiated primordial tooth buds that conform to the genetic instructions encoded for each tooth class. Experimental studies have provided some evidence to support this notion. For example, Kollar and Baird (1969) demonstrated that if tooth buds are removed from their initial location in the jaw and relocated outside their native morphogenetic field, the tooth bud will respond and grow according to instructions encoded for the new morphogenetic field. In other words, it is possible to grow an incisor-shaped tooth out of a molar tooth bud and vise versa.

Van Valen (1970) argued the converse position, for he claimed that tooth buds are prepatterned or genetically encoded. He maintained that gradients in expression are the result of responses to different evocators or triggering agents in different regions of the jaw rather than responses to differing amounts of morphogenetic substance and/or length of tenure within a class or field. Osbourne (1973) reasoned that if the field hypothesis was correct, tooth buds should exhibit identical structure, since they are only differentiated due to external

1 This changed recently with the work of Jernvall’s lab in Finland.

factors. Osbourne (1973) rejected the notions of external control of dental development and primordial equivalency. Instead, he (1970, 1971, 1973, 1975, 1978) and Ten Cate (Osbourne and Ten Cate 1983) argued that the tooth germs form through an internally-driven clonal process. Hence, all tooth germs are already genetically coded for final shape and size prior to the initiation stage of odontogenesis and therefore distal teeth are but cloned replicas of key teeth. Glasstone (1952) and Osbourne (1978) produced evidence that morphogenetic substances are not necessary for determination of tooth size and shape, for if this were so, how can differences in size and shape among members of the same morphogenetic field be explained?

Osbourne (1978) argued that teeth are generated out of three primordial ectomesenchymal clones for the incisors, canines, and molars, respectively. Gradients in shape and size diminunization are a consequence of differential time among the various members of the same morphogenetic field. That is, mesenchyme cells that form the later developing teeth, having divided more than those that initiated bud formation for the key teeth, are marked by a gradient in cell ancestry that results in weak expression of the genetically coded size and shape characteristics. The fact that Glasstone (1952) and Osbourne (1978) demonstrated that tooth buds can be removed from their native environment and still develop into normal crowns of the predicted class indicates a high degree of internally driven genetic control over the size and shape of the permanent teeth. Unfortunetely, such results stand in direct opposition to the findings of Kollar and Baird (1969). So, how can these differences in results be explained? It is possible that such differences are reflections of differing stages of development at which transplantation took place (Brian Hemphill, Ph.D., personal communication 2012).

While Butler (1937, 1939) and Dahlberg’s (1945) field hypothesis and Osborne’s clone hypothesis of development have been supported to some degree by subsequent studies, neither has demonstrated greater reliability for explaining the determinants of tooth development.

Nonetheless, if considered together, these hypotheses and their supporting lines of evidence

suggest that both genetic and environmental factors are involved in odontogenesis (Keiser

1990). Nevertheless, Waddington (1940, 1942, 1957) and Schmalhausen (1949) argued that teeth express a high degree of canalization, or autoregulation, in expression of shape and size.

This autoregulation, once triggered, resists environmental perturbations affecting odontogenetic processes. According to these researchers, canalization increases with each progressive step of dental development to guard against environmental noise. Alberch (1982) identified three levels of genetic interaction involved in odontogenesis. The genome makes up the first level and controls protein production. Second order interactions among enzymes and proteins occur at level two, which control the properties of these mesenchyme cells responsible morphogenesis and induction. The third level of genetic interaction is achieved when the phenotype is expressed. As an epigenetic system, canalization limits deviation from phenotypic blueprints in the face of both genomic and environmental perturbations during odontogenesis.

Sharp (1995) produced evidence that homeobox genes may influence dental development and hence determination of tooth size in mice. Tucker and co-workers (1998) found similar results, which indicated that homeobox gene expression was involved with the timing of signaling molecules controlling dental size and shape in mice.2 Two recent studies have identified genetic markers and homeobox genes involved with odontogenesis. Bailleul-

Forestier and co-workers (2008) demonstrated that MSX1 and AXIN2 genes are associated with the initiation stage of tooth formation and that mutations of these genes lead to dental agenesis, as well as such systemic anomalies as cleft palate and, intreguuingly, colorectal cancer. They also found that AMELX, ENAM, MMP20, and KLK4, and DSPP genes, which are known to be involved in enamel and dentine production, are associated with specific teeth.

Genes associated with tooth development are generally implicated in the development of other ectomesenchymal structures, for Pillas and coworkers (2010) demonstrated that genes associated with odontogenesis also play roles in organogenesis, growth and developmental

2 See also Cobourne and Mitsiadis (2006) and Mitsiadas and Smith (2006) for discussions of the influence of genetic mutations upon tooth development.

processes, and cancer. Each of these genes have different duties of regulation. KCNJ2 is responsible for the development of teeth, jaws, palates, ears, fingers, and toes. EDA controls development of the dentition, hair, sweat glands, and salivary glands, while HOXB and HMGA2 are associated with overall development and stature. Associations between dental abnormalities and cancer were identified for IGF2BP1, RAD51L1, HOXB2, 2q35, and HMGA2 genetic loci (Pillas et al. 2010). To date, researchers at the Tooth and Craniofacial Development

Group of the Developmental Biology Programme, Institute of Biotechnology, University of

Helsinki have identified over 200 genes associated, either directly or indirectly, with odontogenesis in lab studies (results posted online at http://bite-it.helsinki.fi 2011, see also

Sperber 2004, Brook et al. 2009, and Townsend et al. 2008, 2009a). This review of studies of dental development demonstrates that morphogenetic field theory, Osbourne’s theory of clonal development, and at least 200 genes are involved in the odontogenesis of the human dentition.

Heritability of Dental Crown Size

Odontometrics can be an invaluable tool for measuring biological distances between populations, but only if it can be demonstrated that allocation of permanent tooth size is under strong genetic control. The study of dental genetics has its roots in the work of Rutimeyer

(1863), Wortmann (1886), Scott (1892), and Bateson (1894). Rutimeyer (1863) argued that all teeth were controlled by the same factors of heritability. In a remarkable corroboration of

Rutimeyer’s (1863) work, Wortmann (1886) asserted that each tooth was modeled independently, but by a single hereditary factor. Scott (1892) came to the opposite conclusion, for he asserted that the apparent serial homology found in the permanent dentition was actually an artifact of evolutionary convergence; hence, each tooth should be considered an individually controlled phenotypic trait. It was Bateson (1894) who was the first to identify teeth as individual members of a meristic series. Bateson maintained that teeth exhibit serial homology because they are controlled as a unit by a single battery of genes. Hence, differences in tooth size may be assessed quantitatively if one considers all members within such a genetic

unit. Bateson (1894) further argued that tooth size is secondarily constrained by position in the arcade, an idea later elaborated upon by Butler (1937), Dahlberg (1945), and Osbourne (1973).

Use of Twin Studies to Assess Overall Genetic Control of Tooth Size

Heritability of tooth size has been assessed through examination of twins, triplets, siblings, and parent-offspring analyses. Monozygotic twins have been especially important in this regard. This is because if tooth size is a heritable condition, identical twins should express equivalent dimensions regardless of environmental factors such as diet and geographic location

(see for example Lauweryns et al.1993). Barach and Young (1927) were among the earliest researchers to search for heritable conditions of the human dentition in monozygotic twins.

This study was the first attempt to measure the ratio of environmental versus genetic determinants of dental development. Goldberg (1929, 1939) elaborated upon Barach and

Young’s (1927) work and demonstrated that patterns of malocclusion and eruption timing were strongly associated with genetic inheritance, while hypoplasia and caries development were almost completely environmentally induced.

Korkhaus (1930) focused on dental dimensions in monozygotic twins and found high levels of condcordance with the highest level of variability between pairs in the upper second incisors. Cohen and coworkers (1942) later argued that heritability of tooth size is low based upon their investigation of crown diameters in triplets. This assertion was supported by Tobias

(1955), who suggested that environmental factors outweigh genetic factors in the control of tooth size and development. Tobias came to this conclusion because the dizygotic twins in his study often demonstrated a higher degree of concordance in dental characteristics than monozygotic twins. Lundstrom (1948, 1954, and 1955) conducted similar studies, but obtained the opposite results (but see Osborne et al.1958 and Kraus et al.1959), which led him to conclude that mesiodistal crown diameters provide relatively accurate measures of zygosity in twins (Lundstrom 1963, 1969). Genetic variability, in this case, was found to be lowest in canines and highest in the lateral incisors, but not according to jaw as was suggested earlier by

Korkhaus (1930). Horowitz and co-workers (1958) produced results similar to those of

Lundstrom (1955, 1963, 1967) in sets of dizygotic and monozygotic twins, with variability in size heritability ranging from high in the lateral incisors to low in the canines (also see

Menenzes et al.1974).

Bulmer (1970) and Smith (1974, 1975) provided a formula for the estimating genetic control of dental mesiodistal diameters in twins:

h2 = 2 (rmz - rdz)

The formula indicates that the difference between the intraclass correlation coefficients of given pairs of dizygotic or monozygotic twins, multiplied by two, is equal to the degree of heritability squared. The problem with this method was selection of the variables on which to base the calculation of intraclass correlation coefficients, for variability had already been demonstrated in heritability between tooth classes and jaw. Nonetheless, Potter and Nance (1976) employed

Bulmer’s (1970) and Smith’s (1974, 1975) formulae in their study of environmental versus genetic factors on fluctuating asymmetry of tooth size between antimeres. As expected, they found size asymmetry in mesiodistal crown diameters to be strongly associated with factors of environmental perturbation, while heritability of crown size was low in general (but see

Horowitz 1963). Mizoguchi (1977) also identified low heritability of mesiodistal crown diameters for all teeth except the molars, regardless of jaw, among monozygotic and dizygotic twins in

Japan. Potter and coworkers (1976) sought to determine whether mesiodistal and buccolingual diameters were independently controlled. Multivariate statistical results demonstrated a pattern of pleiotropy; that is, these results suggested that the size of all permanent teeth were controlled by a single gene or an exclusive genetic unit. However, the results of their study also indicated that the sizes of the mandibular and maxillary dentitions were controlled by separate genes. Di Salvo and coworkers (1972) investigated the degree of genetic control over the size of deciduous anterior teeth among monozygotic and dizygotic twins living in New

Jersey. Their results indicated a higher degree of variability among dizygotic twins, a result that indicates that heritability is stronger than environmental effects in controlling anterior

tooth size. Nonetheless, these researchers found that their subjects exhibited very low heritability for all anterior teeth, especially maxillary canines.

While Korkaus (1930), Osbourne et al. (1958), Kraus et al. (1959) and Lundstrom (1963,

1967) all provided evidence that tooth size is genetically controlled, environmental factors were never ruled out. This is because developmental instability had already shown potential for affecting tooth size dimensions, particularly in terms of fluctuating asymmetry (Van Valen and

Sloan 1966, Keiser and Groneveld 1991), yet heritability estimates were limited by sample sizes and methodology. While studies based on comparisons of intra-pair differences in monozygotic and dizygotic twins suggested strong heritability of crown diameter size, little was known regarding the degree of environmental influence upon tooth development. As such, research into the genetics of tooth size in twins began to focus less on estimates of variability and heritability and more on determining the ratio of genetic versus environmental variance

(Hillson 1996).

Christian and coworkers (1974, Christian and Norton 1977, Christian 1979) proposed new methods and assumptions for the measurement of the relative contributions of environmental and genetic factors to the expression of phenotypic characteristics. These new methods allowed for the measurement of hidden environmental influences that affect estimation of genetic variance. Potter and coworkers (1979), as well as Corruccini and Potter

(1981), applied these new methods to the study of twin tooth size and asymmetry in American whites. F-tests of total variance identified differences in expression of 15 and 13 variables in males and females out of 56 variables, respectivley (Potter et al.1979). Thus, differences in total variance between monozygotic and dizygotic twins ranged between approximately 23% to 27% based on buccolingual and mesiodistal measurements. Both studies indicated that the degree of genetic variance only differed between monozygotic and dizygotic twins when within-pair mean squares were employed and when combined estimates were controlled for environmental differences.

Evidence for Sex-linked Inheritance of Crown Diameters

Garn and coworkers (1965a, 1965c) measured tooth size heritability based on paired sibling mean product correlations. Their study indicated that X-linkage plays a role in the determination of mesiodistal tooth diameters. This assertion is supported by the fact that higher correlations were associated with sister-sister comparisons than with brother-brother comparisons, while brother-sister correlations in tooth size were lower than those produced by same sex sibling comparisons. These results suggest X-linked inheritance, according to the criteria presented by Mather and Jinks (1963). This is because sisters share their father’s X- chromosome in common, whereas brothers and sisters have the same chances of obtaining a specific maternal X-chromosome (Keiser 1990).

The possibility of X-linked heritability of tooth size was further supported by the work of

Lewis and Grainger (1967) in a study based on parent-child and sibling pairs from 43 families.

Once again, correlations between dental diameters were obtained, but the results are confusing and perhaps inaccurate due to mixed permanent and deciduous sampling. While Lewis and

Grainger (1967: 543) maintained that mixed dentition analysis produces extremely low correlations, thereby making determination of adult tooth size nearly impossible, they nevertheless asserted that “[r]anking of these tooth-specific correlations in decreasing order of magnitude for both parent-child and sibling pairs tends to support the theory of X-linked inheritance of human tooth size.” Goose (1967) conducted a pilot study of mesiodistal and buccolingual tooth size dimensions among 20 families from Liverpool. While this study was intended to demonstrate how mesiodistal dimensions could be predicted from buccolingual dimensions, the results also suggested a pattern of X-linked inheritance of incisor and canine size among these families. Goose (1971) later demonstrated patterns of X-linked inheritance of molar and premolar size among a larger sample of British families. Garn and co-workers

(1965b) reported that up to 90% of variation in tooth size is governed by non-random genetic factors. This study also identified involvement of the X chromosome, but this was only found to be significant in sister-sister correlations. These researchers concluded that, because crown

size and ossification timing exhibit the same trend, females must be equipped with an

“informational redundancy” that reduces variation in dental development.

Potter and coworkers (1968) argued against the use of twin studies in dental genetics and attempted to employ a parent-offspring and full-sib analysis of 153 Pima Indian families.

While earlier studies had demonstrated that total genotypic variability is likely detectable, these researchers argued that environmental factors were also implicated by incomplete estimations of heritability. Analysis of genetic variance based on sib-sib and parent-child comparisons identified three genetic components, dominance, sex-linked, and autosomal. Their study supported the conclusions of previous family and twin studies, but the modes of inheritance

(sex-linked additive, autosomal, or dominance) were not distinguishable from one another; that is, as in previous studies, strong heritability was suggested for tooth size, but could not be identified specifically for any of the detected modal patterns (see also Niswander and Chung

1965). Consequently, they concluded that environmental factors most likely serve to obscure genetic components of control associated with permanent crown size. Nevertheless, Alvesalo

(1971 produced evidence that Y-linked inheritance plays a significant role in the determination of tooth size, for in a later study (Alvesalo 1975) he found that learning disabled subjects exhibiting a 47, XYY karyotype had very large teeth compared with non-affected individuals.

Evidence of Heritability Demonstrated by Family and Sibling Studies

Garn and coworkers (1968) investigated genetic and chromosomal components of crown size profile patterning among 960 pairs of siblings, parents and children, cousins, and random individuals. Subjects were residents of Ohio and comparisons were achieved via product- moment correlations based on normalized t-scores. The use of normalized t-scores corrects for sex differences therby permits same-sex and opposite-sex comparisons. Correlations in tooth size similarity were higher in sibling comparisons (rT= 0.22) than in parent-child comparisons

(rT = 0.20), which produced higher correlation scores than any obtained via cousin comparisons rT = 0.15). Random pairs had a very low average correlation score (rT = .01).

The highest correlations were found to occur among monozygotic female twins (rT = 0.90), a 26

finding upon which Garn et al. (1965b: 1190) based their argument that “crown size patterns have a genetic basis.” This research also yielded evidence of X-chromosome association with tooth size. This is because sister-sister correlations were higher than obtained by any other sibling pairs, while father-daughter comparisons produced higher values (rT = 0.26) than father-son comparisons (rT = -0.04). From their research based on twins, siblings, parent- child comparisons, and cousin similarities, Garn and coworkers (1965b) asserted that inheritance explains 80% to 90% of crown size when the entire dentition (except the third molar) is considered. They further argued that the genetic component of tooth size is controlled by a large battery of autosomal genes along with a small compliment of genes located on the X chromosome.

Alvesalo and Tigerstedt (1974) calculated heritability estimates of tooth size for 90 pairs of full-siblings from Hailuoto Island in Finland. Size variables consisted of mesiodistal and buccolingual diameters for 28 permanent teeth (third molars excluded). Thirty-two of the 56 measures produced heritability estimates greater than 50%, and the overall average heritability for all teeth was 59%. Buccolingual measurements exhibited an average heritability of 67%, while mesiodistal measurements demonstrated an average heritability of 54%. This finding is odd because with all things considered, mesiodisal dimensions ought to be under a higher degree than buucolingual breadths (Brian Hemphill, Ph.D., 2012, personal communication). In all cases and for all teeth, average heritability scores of the maxillary dentition (67%) were higher than average heritability values calculated for mandibular teeth (51%). When considered individually, mesiodistal dimensions of the central incisors, canines, and first premolars express the greatest heritability in the maxilla, while the same is true of the lateral incisors, first premolars and molars in the mandible. Maxillary canines, molars and first premolars consistently demonstrated strong heritability of buccolingual diameters, along with the mandibular first premolars and molars. Some 81% of mesiodistal and buccolingual contrasts indicated that genetic control is stronger for buccolingual diameters than mesiodistal diameters. In addition to providing heritability estimates for tooth size between 54% and 67%,

Alvesalo and Tigerstadt (1974) noted that environmental factors affect the size of distal teeth within morphogenetic fields more than key teeth, a finding that supports Butler (1932) and

Dahlberg’s (1945) field hypothesis (see also Garn et al.1979). However, the field hypothesis is not fully supported for the high heritability estimates produced by second mandibular incisors and second maxillary premolars (Alvesalo and Tigerstadt 1974), suggests a reversal of these dental fields.

Townsend and Brown (1978a) investigated heritability of tooth size in a sample of a geographically isolated polygynous population of aboriginals from Yuendumu in northwestern

Australia. This aim of their study was to identify the role of the sex chromosomes in the determination of mesiodistal and buccolingual tooth diameters. Family analysis was made possible through genealogical charts constructed over the course of 20 years of field research.

Product moment correlations indicated polygenic inheritance but failed to identify any significant heritable component associated with the sex chromosomes. That is, no statistically significant differences in heritability existed between pairs of siblings based on sex. Instead, all comparisons for all teeth indicated heritabilities of tooth size between 50% and 60%, suggesting a strong component of environmental as well as autosomal inheritance operating on tooth size. Consideration of heritabilities among siblings in the same sample by Townsend and

Brown (1978b) produced an average heritability estimate for all teeth of 72% for half-siblings and 81% for full siblings, regardless of sex. This demonstrates that tooth size exhibits a greater degree of similarity between subjects relative to a greater degree of genetic proximity.

Deciduous teeth, in the same population, produced equally high heritability estimates based on sibling analysis of tooth size (Townsend 1980).

Townsend (1985) conducted further research on the heritability of crown size among

Yuendumu aborigines. This study focused on premolars and sought to identify and compare traditional buccolingual measurements with intercuspal distances, for Garn and coworkers

(1977) argued previously that inter-cuspal distances demonstrate a high degree of heritability, and Townsend (1985) tested these assertions, as well as Garn et al.’s (1977) assertion that

mesiodistal and buccolingual size dimensions are determined by the same battery of genes.

The results indicated equivalent heritability for buccolingual (85% to 89%) and mesiodistal

(87% to 88%) dimensions but slightly lower heritabilities for intercuspal distances in molars and premolars (74% to 79%) (see also Townsend et al.2003). Townsend and Brown’s (1985) analysis of half- and full-siblings including two generations of data gathered from 80 fathers,

148 mothers, and 227 sex-pooled offspring. Data was analyzed with nested analysis of variance and calculation of interclass correlation coefficients among pairs. The results yielded heritability estimates between 80% and 91% for full-siblings and between 60% and 99 % for half-siblings. Once again, lack of sex differences in the data indicated autosomal genetic control of tooth size.

Harzer (1987) collected odontometric data from 54 pairs of twins (18 MZ, 36 DZ), four sets of triplets (1 MZ, 3 DZ), their siblings and parents to determine tooth size heritability. The purpose of the study was to identify differences between heritability of individual teeth relative to mesiodistal diameters summed by tooth class (Butler 1932, Dalhberg 1945) and to identify influences of X-linked inheritance of crown diameters. Heritability estimates ranged between

66% and 88% for maxillary teeth and 64% and 81% for mandibular teeth. Overall, the highest heritabilities were found for the maxillary first (88%) and second (74%) incisors, the maxillary canines (74%) and the mandibular first (81%) and second (75%) premolars. Heritability by class produced slightly higher heritability estimates. Butler (1932) and Dahlberg’s (1945) field hypothesis is supported by a pattern of decreasing heritability among the distal members of a morphogenetic field among all teeth, except mandibular premolars. Harzer (1987) concluded that mesiodistal dimensions exhibit greater heritability than buccolingual dimensions (see also

Moorrees 1964). According to Harzer, this is because mesiodistal diameters are more constricted by space requirements, the same condition allowing for greater phenotypic plasticity in the expression of buccolingual diameters. Correlations in size between paired siblings demonstrated the highest degree of similarity in sister-sister comparisons, suggesting some degree of X-chromosome involvement in the determination of tooth size.

Dempsey and coworkers (1995) examined the genetic covariance structure of mesiodistal incisal crown diameters of monozygotic and dizygotic South Australian twins. This study involved a comparison of the buccolingual and mesiodistal incisor diameters of 75 pairs of twins. These researchers sought to identify patterns of genetic control of individual crown dimensions, the genes or groups of genes responsible for the variability of tooth size expression of the incisors, and whether tooth size is controlled independently by jaw. They also tested

Butler’s (1939) and Dahlberg’s (1945) field theory to determine whether heritability is strongest in the key tooth (in this case the first incisor) relative to the distal tooth (second incisor). This was untertaken to address earlier studies that failed to support the field theory (Lundstrom

1948, Alvesalo and Tigerstedt 1974 and Mizoguchi 1977). Dempsey and coworkers’ (1995) univariate and multivariate model identified additive genetic control of tooth size and individually-based environmentally driven variation, a result that corroborates the findings of

Potter and coworkers’ (1983) study of familial dental genetics. Moreover, the results obtained by Dempsey and coworker (1995) indicated that incisors are influenced by the same battery of genes, regardless of jaw, as suggested by Dahlberg (1945). This is because central incisors consistently expressed slightly more heritability than lateral incisors. This was taken by these authors as fact, in spite of earlier work that suggested morphogenetic fields are reversed among the mandibular teeth. General genetic determinants of tooth size were demonstrated to be higher than environmental influences that were expressed individually by tooth, but not by class (but see also Baydas et al. 2005).

Dempsey and coworkers (1995) demonstrated average heritability estimates of 86% for the incisors, regardless of jaw. Hughes and coworkers (2000) conducted similar research involving monozygotic and dizygotic Australian twins. Their study focused on deciduous first and second incisors in an attempt to determine heritability of the deciduous teeth. The subjects’ deciduous incisors displayed heritability estimates of between 62% and 91%.

Mesiodistal dimensions exhibited approximately 15% greater heritability estimates than

buccolingual dimensions. Variability in estimated heritability of deciduous incisors, however, did not conform to the expectations of Butler’s (1932) and Dahlberg’s (1945) field hypothesis.

Dempsey and coworkers (1999) proposed biometrical models for use in family studies of genetic versus environmental determinants of tooth size. Dempsey and Townsend (2001) applied and tested those models on 300 pairs of monozygotic and dizygotic twins of European descent. Dempsey and Townsend (2001) identified strong additive genetic heritability patterns, for all teeth, which explained between 56% and 92% of the phenotypic variation in tooth size based on univariate and multivariate biometrical models. Heritability was identified for all teeth primarily between 80% and 91%, regardless of tooth or jaw. Of all permanent teeth, excluding the third molars, only the mesiodistal dimensions of maxillary first molars demonstrated a high amount of environmentally influenced variability (22% - 27%). Non-additive genetic variation was identified among the canines and first premolars, a condition Dempsey and Townsend

(2001: 692) attribute to “selective pressures acting on these teeth at some stage in human evolution.” Further, they argue that shared environmental influences can influence maxillary first molars due to the early development of these teeth, which occurs around three years of age. Clearly, Butler’s (1932) field hypothesis was not supported by Dempsey and Townsend’s data, as no significant differences were identified between heritability estimates for individual teeth or classes.

Keiser (1990: 29) stated that “the extent of environmental effect on tooth size is ambiguous and remains difficult to assess.” This statement still rings true today. Nevertheless, familial studies, as demonstrated in this chapter, have identified a strong genetic component to the control of adult tooth size. Overall, heritabilities of tooth size, whether teeth are assessed individually, as key units, or as a whole, appear to be in excess of 80% in most populations, suggesting relatively strong and conservative genetic control of the size and shape of the permanent teeth in humans. While the exact genes that control tooth size and the relative contributions of genes located on the sex chromosomes to these batteries are only partially understood, heritability of dental diameters has been repeatedly demonstrated to be effectively

high relative to non-genetic factors. Therefore, because tooth size is under strong genetic control, measurement of biological affinities between human populations is possible via odontometric methods involving measurement and statistical analyses of maximum mesiodistal and buccolingual diameters.

Chapter Three:

Ethno-historic Background

Northern Pakistan stands as a region defined by its geographic and ethno-linguistic diversity (Renseh et al. 2002). The most prominent languages spoken among the ethnic groups in this region are Balti , Burushaski, Khowar, Shina, and Wakhi (Grierson et al. 1919, 1928,

Bailey 1924, Lorimer 1935 – 1938, Morgenstierne 1932). The highland groups that are the primary focus of this thesis include the Wakhi, who are said to be recent immigrants to

Pakistan (Felmy 1994, Sharani 1979), and the Shin, who were a ruling ethnic-class until the introduction of Islam into Gilgit-Baltistan between A.D. 1315 and A.D. 1326 (Biddulf 1880).

The Wakhi

The Wakhi are an ethno-linguistic group who occupy remote areas of Afghanistan,

Tajikistan, Xinjiang, China, and the Karakoram Highlands of far northern Pakistan. Felmin

(1996) asked her Wakhi-Hunzakut informants what defines a member of the Wakhi culture.

Her informant replied that a person knows they are Wakhi if they have spoken the language for as long as they can remember. The Wakhi language belongs to the Pamiri family of languages, which is believed to derive from an archaic eastern Iranian stock known as Ghalcha. Ghalcha, and its eastern Iranian descendants, have been classified into a sub-branch of the Pamir family, which provides a link with Persian Turkmenistan. Nevertheless, linguists remain divided over the classification of the Wakhi language, and this is due to the great dissimilarity between Wakhi and all but two of the other Pamiri languages. Further, the Wakhi language exhibits numerous archaisms that may link them to Indo-Aryan and Dravidian languages spoken throughout peninsular India. In northern Pakistan, the native Wakhi language is spoken universally in the domestic sphere, while Tajik and Urdu have become the written and oral languages of the public sphere. The use of Tajik aligns the Wakhi with Tajikistan,

Uzbekistan, Afghanistan, and Iran, while the use of Urdu reflects the reality that Wakhis living in Gilgit-Baltistan reside in a country where Urdu is the official language. While most Pamiri languages still lack a written form, linguists working to revive and preserve the endangered or 33

dead languages traditionally spoken among ethnic groups in Central Asia have recently formulated written scripts that make use of the Latin, Arabic, or Cyrillic alphabets. Wakhi has been transcribed into phonetic notations, but remains primarily a spoken language used in small towns and within the domestic sphere.

Despite the political boundaries and harsh terrain that separate various Wakhi social groups, Wakhi dialects are not significantly different. That is, Wakhi speakers can communicate with one another easily, regardless of whether they are from China, Afghanistan,

Pakistan, Tajikistan, or Uzbekistan. Four dialects are recognized among the Wakhi populations of northern Pakistan. These include Yarkuni, Yasini, Gojali, and Ishkomani. As expected, dialectical similarities conform to a pattern of isolation-by-distance; Iskomani and

Gojali—the two dialects separated by the greatest geographic distance—exhibit 84% lexical similarity, Yasini and Gojali 89% and Iskomani and Yasini, the geographically most proximate dialects—exhibit a remarkable 91% lexial similarity (Shafiq 2009, Asher 2002, Dani 2007,

Lorimer 1935 – 1938, Morgenstierne 1932).

In Pakistan, Wakhi oral tradition holds that their ancestors lived on the banks of the

Oxus River, that the Wakhan Corridor was their ancestral home, and that they subsequently moved to northern Pakistan and the other areas where they live today. The causal forces behind these historical migrations are largely unknown, except that groups of Wakhi left their ancestral homeland at different times due to wars, the slave trade, natural disasters, harsh taxes, and political oppression imposed by local satraps and Afghan rulers (Shafiq 2009, Nadvi

1990, Biddulf 1880, Nizar Alvi—a Wakhi resident of Passau, personal communication 2012).

The Wakhis living in Gulmit, Pakistan, claim to be descendants of political refugees who fled the Wakhan Corridor during the early 19th century in order to escape persecution

(Felmin 1996). Conversely, Wakhis inhabiting the village of Sost are said to have arrived earlier as conquerors, and to have established most of the towns north of Gulmit as early as A.D.

1680 (Biddulf 1880, Shafiq 2009).

The primary geographic connection between northern Pakistan and the Wakhan Valley is the Broghil Pass. Historically, the Broghil Pass was frequently used by explorers, military surveyors, and spies throughout the 19th century, a practice that culminated during the “Great

Game,” one of the earliest historically acclaimed proxy-wars between Russia and Great Britain.

Moreover, the pass was used by Wakhi refugees forced to flee the Wakhan Corridor during the

1970s, when the Soviet Union occupied Afghanistan and forcibly removed native groups living in the border regions of the corridor. Other links between the Wakhan Corridor and northern

Pakistan occur further east via the Shahgologh, Darwoza, Ochili, and Kankheen passes (Nadvi

1990, Sharani 1979, Alludin 2006, Ashraf 1998).

According to Karim (1997), relations between the rulers of the Badakhshan and

Wakhan regions of Afghanistan, and the Chitral regions Pakistan (located immediately west of

Gilgit-Baltistan) date to the 16th century. Such relations consisted of occasional wars and periods of cooperation marked by migrations of entire communities seeking political asylum.

Chitral, unlike the more isolated northern areas of Gilgit-Baltistan, is historically documented as a long-established trade-link between east and west Asia, once referred to as “Little

Kashghar” (Shafiq 2009). Azzizudin (1987) describes the mutual benefits achieved by trade between groups of Wakhi, who frequently sold horses, saddles, salt, and eating utensils to the

Yarkun of Chitral. Such trade relations were greatly restricted after Chitral became a British protectorate of India in 1885. This move toward annexation culminated between 1892 and

1893 with the formation of the Durand Line, which defined the first geopolitical boundaries between Afghanistan and northern India (now Pakistan). The demarcation of the Durand Line established a buffer-zone between Russia and British India that ended the “Great Game” between these nations for control of the Indo-Iranian borderlands (Irfan 2000, Siddiqi 1996).

Like the Wakhi of Gilgit-Baltistan, the Wakhi of Chitral are immigrants who settled in the region they live today for similar social, political, and economic reasons. The first group of

Wakhis followed their leader, Ali Mardan Khan, who fled Afghanistan during 1870 and sought refuge in Chitral. This was due to the defeat of the Mir of Wakhan by Amir Abdur Rahmen, the

ruler of Badakhshan. A second group of Wakhi immigrants fled their homeland to avoid forced recruitment into the Afghan military that was initiated after the fall of the last Mir of Wakhan circa 1900 (Virsa 2004, Sharani 1979). Upon their arrival, Wakhi refugees were granted small tracts of land in the barren Ishkoman Valley, where Ali Mardan Khan founded the village of

Imit. Kreutzmann (2005) notes that the second migration must have been rather large, since

37% of the population of Ishkoman Valley consisted of Wakhis by 1906. This reflects a fairly rapid change in local demography that speaks to the volatility of population dynamics in this region. Wakhi settlers in Chitral were expected to pay substantially high taxes to the British appointed governor, Ali Mardan, until his death in 1926. The Russian Revolution in 1917 sparked another large migration of Wakhi settlers into Chitral and other areas of Pakistan.

After many migrations between 1870 and 1926, the largest group of Wakhi settlers arrived in the Yarkund Valley of Chitral between 1936 and 1939, once again to avoid oppressive taxes and cumpulsory enlistment in the Afghan military (Biddulf 1880, Kreutzmann 2005, Manzoom

1985, Virsa 2004, Umer 1987). Awan (2004: 110 - 111) notes “It may be pointed out that besides Wakhan, people of adjoining areas like Badakhshan have also migrated and settled in

Chitral.” This fact attests to the continuous dynamism in the local demography of Chitral since

1870.

In 2001, the Islamabad Population Census Organization identified the largest Wakhi population in Pakistan as occupying the Gojal Valley, in the Hunza sub-divison of Gilgit-

Baltistan. The earliest Wakhi settlers may have arrived in the Chipurson Valley during the

16th century, prior to the establishment of the state of Hunza (Census 2001). Baig (1980) argues that upper Gojal was conquered by the Mir of Wakhan, Qutlugh Baig, who established the Ondra Fort circa A.D. 1557, which occupies a strategic position overlooking the villages of

Gulmit and Ghulkin. Prior to Qutlugh Baig’s invasion, upper Gojal had been ruled by Hazur

Jamshid (A.D. 1550 – 1556) and previous Gilgiti rulers. Subsequently, according to oral tradition, the Gilgit princes planned the assassination of Mir Qutlugh, who was poisoned by an old woman. The rulers of Hunza then successfully besieged Ondra Fort and sacked the Wakhi

villages of Gulmit and Ghulkin. The villagers of Gulmit were spared, along with their rulers, but were treated as second-class citizens and slaves by the ruling Hunzika class. Members of the ruling family of Ghulkin were not so lucky, for all were killed or executed after the battle

(Baig 1980, Dani 2007).

After the rulers of Hunza recaptured the Gojal Valley, Wakhis faced heavy taxation and slave labor. That is, Wakhis were forced to work for the state of Hunza, but pay was not provided, nor were public works such as irrigation canals, provided to villages. Although smaller states, such as Gupis and Punial were allowed to remain autonomous for centuries after re-conquest of Upper Gojal by the Hunza, the Wakhis were driven into minority status and never again permitted to form an independent state (Siddiqi 1996).

Like Chitral, but to an even greater degree, the Gojal Valley of Gilgit-Baltistan stands as a crossroads characterized by border connections with China and Afghanistan. Three well- known passes provide connections between Upper Gojal and China. These are: 1) the Peerpik

Pass, leading to the Chipurson Valley; 2) the Minitika Pass, leading to Shimshal; and 3) the

Kunjerab Pass, leading to the Khunzhrav and Misgar valleys. The Khunjerab Pass has often been referred to as “the roof of the world” and is famous for its fierce winters and rugged terrain. Two passes provide entrance to the Gojal Valley via the Wakhan corridor. These are the Kilik and Irshod Passes and they represent the most likely routes taken by the earliest groups of Wakhi settlers who populated Gojal and who are said to have founded the villages of

Gulmit and Gulhkin over 400 years ago (PRHE 2000, Baig 1980, Taylor 1999, Baig ND). Just as in Chitral, Gilgit-Baltistan fell under the aegis of the British colonial authority of India and was recognized a critical strategic location during a time of international strife between Great

Britain and Russia. British officials appointed the first Gilgit agent in 1876, who was commissioned with the surveillance of the borderlands and maintenance of the local populace

(Shafiq 2009, see also Biddulf 1880). The British closed all passes that provided international border-crossings after military officials reported that the Mir of Hunza invited Chinese and

Russian representatives to Pakistan to ask for their assisstance in securing independence from

British colonial rule. Closure of the borders largely prevented contact between Wakhi communities located in different countries until the completion of the Karakoram Highway in

1979 (Khan nd, Usman 1990, GSP 2007).

The Wakhi population of Upper Gojal has been relatively successful when compared to their relatives in Chitral and Afghanistan. This is true in spite of the hardships faced by the Wakhi community after the death of Mir Qutlah and the murder of the ruling family of Ghulhin. The

Wakhi communities of Gojal were responsible for 80% of the taxes paid to the Principality of

Hunza during the 19th century. This represents a considerable inequality given that only 20% of the population consisted of Wakhis (Kreutzmann 2004). Kreutzmann (2003: 230-1) nonetheless argues that Gojali Wakhi have a more stable economy and have achieved greater social equality, for he states that “there could be no bigger contrast than that the Wakhi mountain farmers of Pakistan and Afghanistan.” This is most likely due to the adoption of a very strict subsistence strategy by the Wakhis of Afghanistan, who prefer a harsh, marginal environment to governmental interference. Today, the struggle for existence seems to be a losing battle for Wakhi groups committed to this new subsistence strategy of strict high- altitude farming in environmentally harsh regions beyond the borders of Pakistan (Kreutzmann

2003).

The Wakhi communities of Gojal, however, maintained their traditional agro-pastoral lifestyle.

This agro-pastoral lifestyle has not been as successful among Iskoman and Baroghil Wakhis, who live in relative poverty compared to their Gojali counterparts. In Gojal, where rainfall is scarce, Wakhi depend upon irrigation canals supplied by glacial rivers and streams.

Traditionally, the use of wooden tools and poor maintinence of animals limited success among

Wakhi agro-pstoralists, but recent advances initiated by the Agha Khan Rural Support program have improved greatly the efficiency of this subsistence strategy. The primary contributions of the Agha Khan Rural Support Program have been the introduction of new, more resilient cash crops (potatoes and wheat are two examples), the establishment of micro-credit, and repair or construction of irrigation canals (Kreutzmann 2003, Ministry of Kashmir 2008, Clark 1980).

Most Gojali Wakhis, in addition to maintaining the agro-pastoral lifestyle, also make a living through wage labor. Wakhis are employed frequently as high-altitude mountain hiking guides and porters, but they also find employment as government representatives, and as private organizations (Baig 2002).

A slightly different lifestyle is practiced by the Wakhis of the Shimshal Valley, also located within the Tehsil of Gojal, in Gilgit-Baltistan. Shafiq (2009) describes the cultural tradition of this region as a mix of Wakhi and Burusho customs. The Shimsali Wakhi and

Burusho co-occupy the villages of Aminabad, Shimshal, and Khizarabad. A number of successful major crops are grown in these communities, including wheat, barley, potatoes, beans, and peas. The villages of he Shimshali Wakhi are self-sustaining agriculturally, unlike the villages of the Wakhi found in Hunza which require governmental assistance. In addition to village cash crops, each family maintains a personal garden and fruit trees on their propery

(Shaqif 2009).

While farming plays a significant role in the lives of all Wakhi, Shimshalis focus their efforts primarily upon animal husbandry, maintaining yaks, cattle, sheep, and goats. These animals are important for their hides, meat and bi-products all of which can be utilized in the village or sold for cash (Butz 1996). The custom of keeping yaks yields a cultural distinction between Gojali and Hunzakut Wakhis, for Hunzakuts lack pasturage and they rely heavily on government programs. Consequently, keeping yaks and other grazing livestock is not an option in Hunza or in more developed parts of Upper Gojal. In contrast, the Shimshali Wakhis have access to large tracts of grazing land in the Passau, Batura, and Chipurson valleys.

During 2003, Wakhi high-mountain agro-pastoralists maintained approximately 1000 yaks between the villages of Aminabad, Shimshal, and Khizarabad (Jianliss and Ruijon 2003).

Hence, Shimshali Wakhis specialize in trading yak bi-products and are famous for their yak- hair carpets. As with other Gojali Wakhis, the Shimshalis are famous for their climbing abilities, and work for monetary wages as instructors, guides, and porters (Butz 1996).

In contrast to their cousins in Hunza, the Wakhis of Shimshal retain many of the customs of their purported homeland, the Wakhan Corridor and because of this Shimshal

Wakhis traditions have not been affected by those of Hunza. This is demonstrated by festivals not observed by Wakhis living outside of the Tehsil of Gojal. One such festival is the Kit Dit, which celebrates the beginning of spring. Other Gojal-specific festivals include the bird-festival

(Winga-Stuai), the harvest (Chineer) and seed (Tagham), as well as the Kuch, which celebrates cleanliness and purity of streams. The spring festival, Hoshligram, is specific to Shimshal

Wakhis, as is the end-of-ploughing-festival (Spunder Vishing). However, all Pakistani Wakhi, however participate in the Nauroz festival, which celebrates their claimed Iranian (Persian) heritage (Tarar 2000, Shaqif 2009). Shafiq (2009) points out that Wakhi customs in Gojal are scheduled to emphasize and promote communal efforts on dates that are important for the agricultural cycle.

Despite the Nauroz festival and its associations with Persian heritage, the Gojali Wakhis retain many of the Central Asian traditions of their alleged homeland, the Wakhan Corridor.

This is reflected by the commonality of family names, such as Baig, Chahrah, Begum, Roshan,

Bibi, and Khatoon. Two factors, work immigration and arranged marriages, account for the relationship between Pakistani highland Wakhis and Central Asians. The first, work immigration, was initiated during the 14th century, when the Mir of Hunza began to hire armorers from Badakhshan, such as the purported ancestors of the inhabitants of Madaklasht in Chitral (see Hemphill 2010, 2012) and carpenters from the Wakhan Valley and Kashgar.

Additionally, Central Asian trade caravans often visited Gilgit-Baltistan and brought with them skilled laborers, many of whom were hired by the local Mirs. Another attraction for outsiders were the Sufi shrines; foreign pilgrims visited these shrines frequently, especially that of Baba

Ghundi in the Valley of Chupason. These ties to Central Asia were strengthened by arranged marriages between the ruling families of Hunza, Chitral, Badakhshan, Baltistan, and Serikol,

China (Dani 1995).

While the traditional festivals of Hunza are practiced by the Wakhis living who live there, Hunzi (2002) argues that Wakhi culture is still evident in the housing styles. In fact, the

Wakhi influence on the architechure of family homes has all but replaced the traditional Hunza

Burushikitta, with Goshi Ha, semi-subterranian houses commonly built in the Wakhan Valley.

The Shin

The Shin are an ethno-linguistic group who occupy the valleys south of the territories occupied by the Wakhis. The Shin speak Shina, a Dardic language (Lorimer 1927). While

Biddulf (1880) referred to the main “caste” of Shina-speakers as the “Shin,” living Pakistanis who speak this language trace their identities to different villages, or regions, rather than by linguistic association (Radloff 1992). As noted by Goodenough (1951), the concept of identity is important, for it is culturally defined and is mitigated by individuals’ roles in society - roles that also can be described as multiple layers of duty-status obligations (Binford 1971, also see

Goodenough 1965, Linton 1938). Hence, it is important for the current study of biological affinities to establish the criteria that define ethnic groups that have previously been classified based on language instead of location and historical background. It is also important to ask the question as to whether regionally separated groups of Shin can be considered to have shared origin. The best place to begin is linguistics. Here may be found some of the nuances that separate different groups of so-called “Shin,” all of whom were lumped by Biddulf (1880) into one regional ethnic identity.

The language known as Shina is one of the most thoroughly documented of the northern Pakistani languages (see for examples: Grierson 1919, 1924, 1925; Lorimer 1923 –

1924b, Bailey 1924b, 1927, 1925b, 1925c, 1928 – 1930b, 1930-32; Wilson and Grierson 1899,

Turner 1927, Cunninham 1853, Leitner 1893, Biddulf 1880, Barth and Morgenstierne 1958,

Schomberg 1935, Morgenstierne 1932, 1945; Franke 1907, Muhammad 1905, orimer 1923-

25b, Radloff 1992, Ramaswami 1975, Schmidt and Zarin 1981, Rajapurohit 1983, Namus

1961, Ramaswami 1982, Schmidt 1985a, Hook 1990, Edelman 1983, Koul and Schmidt 1984,

Shahidulla 1964). Much of the attention placed upon the Shina language has been because

Shina, as an Indo-Aryan language, has been used to demonstrate the efficacy of a Dardic classification within the Indo-European family of languages to demarkate its relationship to

Sanskrit and other languages of great antiquity. In spite of this focus on the Shina language, its structure and its origins, little work has been done on differences between dialects of Shin, until Radloff’s (1992) Linguistic Survey of the Northern Areas.

Lorimer (1927) was one of the first to examine regional differences in Shina dialects. In fact, Lorimer found no similarities between the eight regional dialects with regard to transitive verb forms. Lorimer’s (1927) work suggested that many unknown dialects likely existed in

Gilgit-Balstistan beyond Gilgit’s influence and accessibility at that time (also see Lorimer 1923

– 1925a). Instead, Lorimer argued that isolation played a prominent role in the formation of unique Shina dialects in the region of Chilas (see also Bailey 1924a). While over the course of time many subsequent studies have followed those of Lorimer in seeking out different dialects of Shina (see Skalmowski 1985, Voegelin and Voegelin 1965, Zograph 1982, Jettmar 1980,

Gardezee 1986, Parkin 1987, Vohra 1981, Buddruss 1964, Nayyar 1984, Snoy 1975, Zarin and

Schmidt 1984, Namus 1961, Fussman 1972, Koul and Schmidt 1984, Schmidt 1984, 1985b,

Fitch and Cooper 1985, Voegelin and Voegelin 1977), none focused primarily on the relationships between them.

This lack of focus has been addressed more recently by Radloff (1992), who conducted a linguistic survey in Gilgit-Baltistan and Chitral to identify languages and their regional dialects. Radloff queried, with regard to the Shina language, “What dialects of Shina actually exist, where are they current[ly spoken], and how [are] they related to [one another]?” (92). A second purpose of Radloff’s work was to determine whether Gilgit Shina was considered the standard dialect for all Shina speakers, as was asserted by Lorimer (1927). This question is opportune because Radio Pakistan brodcasts in this dialect, newspapers and books are dominated by this dialect, and Gilgit itself stands as one of the largest trade centers in Gilgit-

Baltistan. Further, by logical extension, Radloff (1992) sought, through questionnaires, to determine whether the Gilgiti dialect is mutually intelligible to non-Gilgiti speakers, and how

they “classify” the Gilgiti dialect in relation to their own. The same question was asked in relation to the Shina dialect spoken by residents in the Chilas region, who claim the ancestral home of the Shin and the Shina mother tongue as being that of the dialect spoken in Upper

Chilas. In addition to conducting interviews and questionnares, Radloff (1992) examined lexical similarities between dialects based on word lists and written texts. Radloff identified distinct

Shina dialects in various regions, yet levels of mutual intelligibility were high. Radloff’s study also demonstrated that bilingualism is prevalent across Gilgit-Baltistan and that second language influences may be a leading factor in dialectical differences between groups of Shina speakers. Finally, Radloff’s data suggest that Gilgiti-Shina, in fact, is the standard dialect, even among populations living in the region of Chilas.

With this is mind, it is important to consider the possible homeland of the Shina language, and by association perhaps, the Shin people. Biddulf (1880) places much emphasis upon the Shin and their “Dardic” ancestry in his Tribes of the Hindoo Koosh (also see Moorcroft

1880, Vigne 1835). Importantly, for this study, he argued that the Shin were originally all of one race with origins outside Gilgit-Baltistan. Biddulf (1880) asserts, based on communication with Shin informants and British military records, that Shin conquerors entered Gilgit-

Baltistan and the surrounding region via the Indus Valley some time prior to A.D. 1680. The

Shin, he argued, were militant invaders from the south, who either displaced the local populace or forced the Shina language upon them. Moreover, Biddulf claimed that the original inhabitants of Gilgit-Baltistan were Yashkuns, who spoke Burushaski, a linguistic isolate attributed to the Burusho. Some Yashkun who lived in isolated valleys, continued to speak

Burushaski, while those living in more accessible areas had to adopt Shina, though most

Yashkuns today are fluent in both languages. While Biddulf’s assertions were based on historical texts and the testamonies of local informants, this hypothetical explanation for the spread of the Shin and the Shin languages into the Gilgit Valley was offered independently by

Lorimer (1923-25a) based on his linguistic analysis (see also Radloff 1992, Bashir 2010).

Biddulf (1880) presented the Shin, Yashkun, and Burusho in terms of what might be described as “pseudo-castes,” for he believed these groups retained prominent aspects of

Hinduism and Zoroastrianism, which their ancestors must have practiced prior to the entrance of Islam. That is, the Shin occupied the highest status among these groups, followed by the

Yashkun and then the Burusho. However, all of these groups were considered lower in status than the neighboring Balti, who Biddulf described as descendants of a later wave of Tibetan conquerors (see also Guzman and Hemphill 2012).

Another important observation made by Biddulf (1880), and echoed by Lorimer (1923-

1925a), is that the Gilgiti-Shina dialect is marked by a strong similarity to Burushaski.

Lorimer suggests that Gilgiti-Shina and Burushaski are not morphologically similar, but resemble one another in style of oral expression (for example: pronunciation and accentuation).

Biddulf attributes this condition to centuries of what today would be referred to as borrowing and code-switching among the Yashkun, who adopted the Shina language, but who originally spoke Burushaski. Moreover, Biddulf argues this was reinforced through inter-marriages between Shins and Yashkuns, for the caste-like relationships between these groups provided marriage rules by which Shin men could take Yashkun wives, but Yashkun men were forbidden to take Shin wives. In this way, the Shin and Yashkun formed marital moieties that relied upon a social organization predicated, perhaps, on what could be described as linguistically defined clans. Over time, these marriage practices led to the formation of a distinct Gilgiti dialect of Shina not found in the valleys of Skardu and Astore, where the

Burusho are not to be found and the Yashkun are a minority. This explanation was supported by Budress (1985), who argued that the Gilgiti dialect of Shina and Burushaski exhibit near- identical phonological structures.

Radloff (1992) proposes a near identical model to explain the relationship between the eastern Shina dialect and the Balti language. This is because Biddulf (1880) also described an invasion of Tartars that invaded and seized control of the Skardu Valley. He proposes that these Tartars were the ancsestors of the Balti, who represent today the predominant

population in eastern Gilgit-Baltistan. While no date for such an invasion is mentioned by

Biddulf, Radloff (1992) argues that a Balti-speaking invading population likely pushed the local

Shina-speaking population into the high-river valleys of the region, such as Garkhon and Dah.

Therefore, the eastern Shina dialect, which contains many loan words from Balti, is spoken within these valleys in more accessible areas and trade centers where Balti is spoken as a sort of lingua franca. Another reason for the presence of Shina speakers in predominantly Balti territory is that Shin prisoners from Chilas and Astore were brought to eastern Baltistan via the slave trade (see Biddulf 1880 and Leitner 1866).

A recent linguistic study by Schmidt (1984) identified linguistic convergence between

Shina and Balti among village populations identified by Biddulf (1880) and Leitner (1866) as the founding Shin families in Baltistan and Ladakh. Radloff’s (1992) socio-linguistic survey provides further support for these assertions as unique dialects of mixed Shina and Balti are found in these regions. In fact, most so-called “Shins” living in eastern Gilgit-Baltistan speak

Balti primarily, but reference Shina as their native tongue. In a caste-like relationship similar to that of the Shin, Yashkun and Burusho (if the Yashkun and Burusho do indeed represent separate populations), Balti men traditionally took Shina-speaking wives, but Shin men were forbidden from marrying Balti women (Biddulf 1880).3

This distinction in social status is further demonstrated by the use of the term “Brokpa” among Baltis. This term is employed by Baltis to describe those who speak Shina in Baltistan.

Moreover, Shina-speakers consider this a negative connotation (see also Biddulf 1880, and

Schmidt 1984, who calls eastern Shina “Brokskat”). In fact, this term was abandoned by

Radloff (1992) because his Shina-speaking participants informed him that word is racially discriminatory and is never used by them to describe themselves. In fact, the word implies racially-driven slavery, in a similar fashion to a word “nigger” once used by Europeans and

3 It should be noted, however, that such mixed ethnic group marriages were by far a minority of all marriages among the Shina, Yashkun, and Balti.

Americans to describe African slaves brought to the New World. Ramaswami (1982) assigns a similar designation to the term “Brokskat,” which is only properly applied to Shina-speakers living near Garkhon in Ladakh (See also Rajapurohit 1983). More recently, other languages have produced differences between regional Shina dialects. For example, there has been a greater influence of Urdu upon northern Shina dialects, while Pashto has heavily influenced the southern Shina dialects (Schmidt 1984, Radloff 1992). With so much confusion over the origins of the Shina, it stands as a fundamental question as to whether an ethnic identity can be constructed based on the distribution of this language—after all, who can be considered the original speakers of Shina? Radloff’s (1992) informants implicated the regions of Chilas and

Astore as the homeland, but Gilgiti appears to be the standard, which seems odd because the

Gilgiti-Shins are a minority there. The standardization of Gilgiti Shina seems unlikely given

Schmidt’s (1984: 680) argument that “sectarian differences between the Gilgitis and their

Shina-speaking Sunni neighbors have undoubtedly contributed to the differences in dialect, as they have intensified the isolation between Gilgit and its neighbors.” Nonetheless, while the socio-politically and geographically separarated Shina-speaking populations have specific designations for their own village communities today (Gilgiti, Astorei, Haromoshi, etc.), they all consider themselves relatives (Radloff 1992) and their true origins, or those of their ancestors, remain as unknown to them as they do to science.

While much has been written here about language and history, more is necessary. This is because one of the primary foci of this thesis is to test whether membership within a linguistic lineage parallels ethnic identity and biological distinctiveness. It will be interesting to see, in this case, whether the Shina speakers located in Haramosh, Gilgit, and Astore share closer biological affinities to one another than to their neighbors, such as the Wakhi, and

Madaklasht, and to a lesser degree the Swatis of Mansehra District located in the foothill zone to the south. It will be even more interesting if such affinities run parallel to dialectical similarities and differences that have been attributed to geographic proximity of founding populations, linguistic merger with neighboring languages, and socio-geopolitical isolation of

different regions of Gilgit-Baltistan at different times throughout history. But first, how many

Shina dialects have been identified?

While Leitner (1880), Grierson (1919), and Lorimer (1927) produced lexical diagrams to represent differences in Shina dialects, such dialects were not confirmed to be regionally sound by these studies; hence the number of dialects could not be determined. Bailey (1924a) addressed this lack of classification based on analysis of grammar and phonology. He identified three Shina dialects, Gilgiti, Kohistani, and Astorei. These regional designations were accepted until later scholars replaced “Kohistani” with Chilasi (Namus 1961, but see also

Schmidt 1985b). This classification presents an opportunity to test associations between linguistic affinities and biological affinities in the current study, but there is more. Sub- dialects of these three Shina regional dialects were identified as well. Grierson (1919) adds a northwestern dialect, a variant of Gilgit, which was later identified as the Puniali dialect by

Namus (1961) and Schmidt (1985b). Lorimer (1927) added the Kuhi dialect and asserted this was used west of Punial, while all the linguists cited here agree that the Shina spoken in the

Chilas, Tangir, Darel, Jalkot, Palas, and Kolai Valleys represents the Chilasi, or Indus

Kohistani dialect group. Conversely, the Astore dialect is represented by three regional sub- dialects found in the Astore, Guresi, and Drasi Valleys. Radloff’s (1992) linguistic survey of

Gilgit-Baltistan produced results consistant with previous studies, in which four regional dialects were identified: 1) Northern Shina; 2) Eastern Shina; 3) Diamer; and 4) Kohistani.

Bidduf (1880) mentioned the infamous “Brokskat” dialect, which was retained in these later studies for native Shina speakers living in the Ladakh Valley of India, in which they make up a minority of a much larger population dominated by Baltis and Guijars (see also Leitner

1880, Ramaswami 1975, 1982; Joldan 1985). Ramiswami (1982) and Schmidt (1984) determined that the Shina spoken in Ladakh is not mutually intelligible with any other Shina dialect. Interestingly, Brokskat was known to the Guijars as the language of the “Buddhist

Dards” that invaded their valley during the early historic period. Ethnographic records indicate that Broskat was abandoned by most of its speakers in favor of Ladakhi; a Ladakhi term,

Brokskat means “mountain dwellers” (Joldan 1985). Hence, further discussion of this dialect will be left for future scholars, who have interest in the Ladakh region and the ethnic groups living there.

In the current study, dental samples were obtained from individuals living in Gilgit and

Haramosh that use the Gilgiti dialect, and from individuals living in Astore who speak the

Astorei variant. These groups, like other regional groups of Shina speakers, refer to themselves by village and region, rather than as “Shina.” This is because none of the regional groups,

Astorei, Gilgiti, and Kohstani identify with each other. Even sub-dialect groups place themselves in ethnic sub-divisions, as demonstrated by the inhabitants of Satpara, who say

Astoreis are their cousins, but that they themselves are Satspari. All subgroups apparently identify themselves as ancestors of the first Shina speakers and each of their respective regions as the original Shina homeland. For example, the Kohistani and Chilasi consider themselves close relatives, but as two distinct surviving decendant populations of the original Shina speakers. The Kohistan-Chilas region contains the Shinaki valley, thought to be the homeland of all of the Shin “tribes” (Radloff 1992).

Overall, this review demonstrates that while linguists and historians have considered language and personal accounts reliable criteria for ethnic classification of Gilgit-Baltistan populations, the people themselves do not always, or even often, or even at all, in some cases, identify themselves in this way. For modern speakers of Shina, identity is related to the villages and regions in which they live. This is important, once again, for the current study, which seeks to test whether ethnic classifications based on the language, culture, and historical accounts that are commonly used in demographic studies are supported by the patterning of phenotypic expression of biological variation among these populations.

Chapter Four:

Previous Anthropometric, Anthroposcopic, and Genetic Research

The biological classification of humans was first approached scientifically by Linnaeus in 1735, who argued that hair and skin color are classifiable traits that can distinguish between different “races” of people. This notion of biological races was inspired by the earlier work of Bernier (1688), who identified four biological types among humans: “white Europeans, black Africans, yellow Asians, and Lapps” (see Kalla 1994: 2). This notion of racial typology pervaded early anthropometric studies that sought to identify racial types among various populations. The apogee of racial typology was captured in the work of Hooton (1956), who identified three racial types to which he assigned the nomenclatures of caucasoid, mongoloid, and negroid (see also Olivier 1969). These hypothetical “pure races” were further divided into sub-races and assigned morphological types based on physical characteristics.

Anthropometry and Anthroposcopy

Malhotra (1978) identifies the earliest anthropometric and anthroposcopic studies of

South Asian populations as occurring in 1868. Ujfalvy (1884, 1886) and Aurel Stein (1916,

1928) conducted such work in the Karakoram Highlands, while Flower (1885), Waddell (1900), deTerra (1905), and Thurston (1909) were among the first researchers to classify Indian populations based on anthropometric measurements. Risley (1915), von Eickstedt (1934), and

Guha (1935) documented biological variation across the entire Indian subcontinent and introduced new methods for the regional classification of racial types (see also Giufrida-Ruggeri

1917, Haddon 1924,1929, Hutton 1932, Mahalinobi et al. 1949, Majumdar and Rao 1960,

Karve and Dandekar 1951).

Risley (1915)

Sir Herbert Risley directed the Census of India anthropometric project during 1901 and published the results in his famous 1915 work The People of India. This work was the first attempt to classify physical types, or races, among the myriad castes and tribes across the entire Indian sub-continent. Risley’s analysis was based on cranial morphology, nasal index, 49

facial morphology, stature, and skin pigmentation. This data was collected from human subjects living in Bengal, the Northwest Frontier Province, Punjab, Baluchistan, Rajasthan,

Maharashtra, Bihar, and Orrisa. These identifications reflect later political divisions of India and Pakistan, not the political divisions of the British Raj present at the time of Risley’s survey.

Waddel (1900) and Thurston (1909) conducted similar research in the Northeastern Hill

Provinces and the Madras Presidency, respectively.

Risley’s (1915) biometric survey of India led to the identification of seven racial types

(see Figure 1). Three generalized “pure” physical types were distinguished based on anthropometric and anthroposcopic observations: Mongoloids, Indo-Aryans, and Dravidians, with the latter two receiving nomenclature primarily via linguistic association. Four additional sub-types were identified, Aryo-Dravidian, Monglo-Dravidian, Scytho-Dravidian, and Turko-

Iranian. According to Risley (1915), these groups represent products of admixture, or miscegenation, between members of the three “pure” races.

Risley maintained that the “pure” Dravidian type reflects the original population of

India. Represented in blue in Figure 1, the Dravidian type has the widest geographic distribution, which extends between Ceylon (Sri Lanka) in the south, across all of the Madras

Presidency (Tamil Nadu), Hyderabad (Tamil Nadu), east central India and Chotta Nagpur in the north. Risley presents the Telugu-speaking Sholugu (Figure 2, K) and Kadir tribals (Figure 2, L) of Andrha Pradesh as classic examples of the Dravidian type. According to Risley (1915), individuals of the Dravidian type exhibit dark, plentiful, slightly curly hair, and are of relatively short stature. Additionally, the Dravidian type is characterized by very dark or black skin, long heads, broad noses with slight depressions of the root, and very long arms. Risley concluded that living Indians of the Dravidian racial type represent the original occupants of India, who after initial colonization of the sub-continent, experienced various degrees of admixture with members of the Persian, Aryan, Scythian, and Mongoloid races either due to subsequent invasion or contact via trade (also see Kalla 1994). This type should not be confused with the

Figure 1: Racial Types of India according to Risley (1915), slightly modified from the original text.

Negrito type (Figure 2, A), which Risley (1915) believed to represent a foreign population from

Africa.

The Scytho-Dravidian type represents admixture between the Central Asian Scythian racial type and indegenous Dravidians. Groups assigned to this type include Marathas

Brahmins, Kunubis, and Coorgs. Risley (1915) argues that individuals within castes of higher social status exhibit greater Scythian ancestry than those found in the lower castes, which are dominated by Dravidian ancestry. According to Risley, members of this type are characterized by medium stature, a fair complexion, broad heads, a fine, inconspicuous nose, and scanty facial hair. This type is distributed across west India (Figure 1, Figure 2, M).

According to Risley (1915), members of the Mongoloid type are characterized by a broad head, dark skin with a yellow tint, a lack of facial hair, short stature, eyelids with epicanthic folds, flat faces, and broad to fine noses (Figure 2: F,G, H and I). The distribution of members of the Mongoloid type are depicted in yellow on Risley’s (1915) map (Figure 1), which shows them distributed in an area ranging from the western reaches of Nepal to Myanmar and Assam in the east. Risley (1915) argued that members of the “pure” mongoloid type are not found anywhere in India. Instead, admixture between races living on opposing sides of the peripheral borders of the sub-continent has resulted in a small variety of mixed races in these regions.

Members of Risley’s (1915) Monglo-Dravidian type are characterized by broad, round heads, dark skin, facial hair, flat noses, and medium stature. This racial sub-type is found primarily in Bangladesh and East Bengal (Figure 1, green, Figure 2, E and J).

Members of Risley’s (1915) Indo-Aryan type are distributed across the Punjab,

Rajasthan, and Kashmir provinces of northwest India, and the highlands of northern Pakistan

(Figure 1, designated in pink). Ethnic groups classified within this racial type include Rajputs,

Jats, Khattris, and Kashmiri Brahmins (Figure 2, C). Risley (1915) attributed the following complex of anthropometric and anthroposcopic traits to members of the Aryan type: fair complexion, dark eyes, luxuriant facial and body hair, dolichocephalic heads, tall stature, and prominent noses. Risley (1915) argued that the uniform presence of these features, which have

no homologue in any other Indian race, suggesting that groups classified under the Aryan type are most likely recent immigrants who experienced little admixture with penninsular Indians.

According to Risley (1915), members of the Turko-Iranian type are characterized by a complex of traits that includes fair complexion, dark and grey eyes, abundant hair on the head and face, and broad heads. Like the members of the Aryan type, the Turko-Iranians exhibit a narrow, prominent nose, but the conformation is long, rather than short (see figure 2, D).

Risley (1915) argued that Brahuis, Baluchis, and Afghans represent classic examples of the

Turko-Iranian type, which he believed was the product of admixture between Persians and

Figure 2: Risley’s (1915) examples of the seven Indian racial types.

Turks. Risley identifies members of the Turko-Iranian type as being distributed, according to

Risley (1915), across Afghanistan and Pakistan (Figure 1, designated in orange).

The last and perhaps most controversial of Risley’s (1915) racial types are Aryo-

Dravidians, or Hindustanis. Members of this type are distributed across the Punjab to Bihar, and form a racial border with Mongolo-Dravidians of Bengal (Figure 1). Risely (1915) argues that the proportion of Aryan admixture declines relative to caste affiliation. Consequently, higher status caste Aryo-Dravidians are distinct from lower caste or tribal members (Figure 2,

B) of the Aryo-Dravidian type, the latter of which may be confused with members of the pure

Dravidian type. Risely (1915) argues that the Aryo-Dravidian type, characterized by dolichocephalic head shape, light brown skin, short stature, and moderate nasal breadth, is the product of an Aryan expansion eastward and southward across peninsular India, that occurred subsequent to the initial invasion of North India, and resulted in significant admixture between Indo-Aryan and Dravidian-speaking populations.

Giufrida-Ruggeri (1917)

Giufrida-Ruggeri (1917) presented an alternative, six-tier classification of Indian ethnic types. These six ethnic elements are defined as Negrito, Pre-Dravidian or Australoid Veddic,

Dravidian, Tall Dolichocephalics, Brachycephalic Leucoderms, and Dolichocephalic Aryan.

Giufrida-Ruggeri (1917) based his classifications primarily on skin color, cranio-facial metrics and stature. Based on the results of his observations, Giufrida-Ruggeri (1917) argued that the

Veddahs and other South Indian tribals exhibit pre-Dravidian or Australoid Veddic characteristics with a hint of Negrito admixture. In agreement with Risley (1915), Telugu- and

Tamil-speaking ethnic groups of South India were classified as Dravidians. The Todas, however, due to their tall stature, were assigned a type of their own, “Tall Dolichocephalics.”

Giufrida-Ruggeri classified Indo-Iranians and Indo-Afghans as “dolichocephalic Aryans,” assigning to them a separate species, Homo dolichomorphus. Finally, Giufrida-Ruggeri (1917) argued that Armenians, ethnic groups speaking Pamiri languages, and the Caucasoid peoples

of Georgia represent a classic type that can be assigned to the Aryan invaders of the Rig Veda.

He dubbed them “Homo Indo-Europeans brachymorphus”4.

Haddon (1924, 1929) Haddon (1924, 1929) rejected Risley’s (1915) racial categories and proposed classification of Indian ethnic types based on geographic, rather than linguistic criteria. While

Giufrida-Ruggeri’s (1917) study was riddled with errors, Haddon (1924) agreed, with his assertion that there must be a pre-Dravidian type. Haddon (1926, 1929) identified three racially defined geographic regions, the Himalayas, the northern plains, and the southern jungle plateau. Haddon (1929) argued that the original occupants of India were the ancestors of the jungle tribes of the Deccan plateau. This type, he argued, was once distributed across

India and is characterized by long headed, dark skinned, black haired individuals of short stature. Haddon (1929) argued that all South and West Indian tribal populations and members of the low castes of Telugu, Tamil, Malayalam, and Canarese-speaking “Dravidians” fit the pre-Dravidian type. Two hypotheses were put forth to explain the arrival of Dravidians:

Apart from language, there is a general culture which is characteristic of these people, and after the elimination of the pre-Dravidians a racial type emerges with finer features than those of the aborigines and the conclusion seems evident that this was due to an immigrant people who reached India before 2,000 B.C. There are two views respecting this with hypothetical invasion; the one is that these people came overland the Brahui marking their route, the other is that they arrived by sea. Apart from the dark colour of the skin there are many points of resemblance between the Dravidian and Mediterranean peoples which point to an ancient connection between the two, perhaps, due to a common origin (Haddon 1929, cited in Kalla 1994: 63).

Members of Haddon’s Dravidian type are characterized by dark skin, a wide nose, dolichocephalic head shape and medium stature. Haddon (1929) argued conversely to Risley

(1915) that members of high caste South Indian groups are associated with the Dravidian type, while members of low status castes and tribes represent examples of the pre-Dravidian type.

According to Haddon’s (1924, 1929) anthropometric studies, admixture between Dravidian and

4 Giufrida-Ruggeri incorrectly relates that the concepts of race and species have equivalent ramifications. The implication here of species-level separation between ethnic groups is patently absurd.

pre-Dravidian types is evident among the middle-status caste groups. As such, Haddon proposed that the Dravidian type represents a foreign element of the Ganges Valley, while the pre-Dravidian type represents an indigenous element once widespread across the entire subcontinent. Haddon (1929) further mentioned that the negrito element of South India originated among aborigines of the Andaman Islands.

Haddon (1929) was one of the first to argue that archaeological samples are necessary to reconstruct a population history based on anthropometrics. Like Risley (1915), Haddon

(1924, 1929) defined an Aryan type that he attributed primarily to the Jats and Rajputs of

Punjab, Rajasthan, and Kashmir. Haddon (1929) argued that while prehistoric skeletal evidence was lacking, living populations of the “Aryan” type are the most likely descendants of the alleged Arya Invaders of the Vedic literature. This population movement had its origin or origins in West or Central Asia and was likely a gradual process. Such a migration, according to Haddon (1929) would have progressed rapidly though what is now Pakistan and the Punjab province of northern India, but would have progressed slowly through the Rajasthan desert and the thick forests south of the Himalayas. Consequently, the Aryan type is divided into two sub-types.

According to Haddon (1924, 1929) members of the Veddic-Aryan type are represented by living high-status Brahmins of North India, but the current populations of Pakistan are the product of post-Veddic Scythian, Parthian, Chinese, Tibetan, and Mongolian invasions mixed with an Aryan, Kshatriya caste element. These invading groups were absorbed into the indegenous Dravidian and pre-Dravidian populations.

Haddon (1924, 1929) classified the Jats and Rajputs of Punjab, Rajasthan and the

Kashmir Valley as either Indo-Aryans or Indo-Afghans. Individuals assigned by Haddon to this these types are marked by light brown skin, straight, fine leptorrhine noses, long faces, developed foreheads and dolichocephalic head shapes. According to Haddon (1924, 1929), these non-Veddic types are the descendants of several invading populations from Central Asia.

These include the Scythians, who invaded India circa 150 B.C. and conquered the Peshawar

region at the northwestern border of South Asia. This invasion allegedly introduced proto-

Nordic elements into the populations of Pakistan. Subsequent invasions involved the Parthian occupation of Sindh; Alexander’s Greeks and the armies of Parthia and Persia are recorded historically as conquerors of the Northwest Frontier Province of India. Further, the Kushans allegedly invaded India and occupied the northern regions between A.D. 20 and A.D. 178 (Kalla

1994). This was followed by a short incursion of the White Huns (or Sakas), which was ended when the Turks conquered and annexed the Hun empire in A.D. 526. These invasions, according to Haddon (1924, 1929), account for the complex of “Indo-European” traits found among members of the non-Veddic Aryan type. Haddon assigns the Indo-Afghan type to the high status caste populations of the Gangetic Plain. This type represents admixture between historically documented Moghul invaders and the aboriginal population. Dravidian characteristics are more frequent among members of the lower status castes (Haddon 1929).

Haddon argued that Risley’s (1915) Scytho-Dravidian type was based only on the presence of brachycephalic heads among them, while members of his (1924, 1929) Afghan type possessed a number of features of the “Alpine type” of Eastern Europe and West Asia, including tall stature, long faces, fair skin, and blonde hair. Stein (1916, 1929) documented the presence of this brachycephalic type among the Wakhi and Kirghiz of Afghanistan, and noted the Nordic-

Alpine influence among a distinct class of “Pamir groups” that Haddon (1929) claimed likely represent the population of origin from which traits of the Indo-Afghan type were derived. As further support for this assertion, Haddon noted the lack of any Mongoloid traits among them.

Turning his attention to the northeastern region of India, Haddon (1929) argued that

Tibeto-Burmese were the first of the Indo-Chinese populations to enter India, via Assam and east Bengal (Bangladesh). The tribal populations of Assam, according to Haddon (1929), contain within them traces of several racial elements. These include 1.) an ancient, pre-

Dravidian dolichocephalic platyrrhinic physical type exhibited by the Manapuri, Miki Kachari, and Kuki, tribes, 2.) a Nesiot, dolichocephalic mesorrhinic type attributed to the various Naga tribes, 3.) a mesocephalic platyrrhinic element exhibited by members of the Khasi tribe,

southern Chinese, and the inhabitants of Myanmar, 4.) a mesocephalic mesorrhinic element found among the tribal populations of Assam as well as members of Hindu castes of Bihar and

Bengal, 5.) a Eurasiatic element of brachycephalic leptorrhines mixed with a brachycephalic platyrrhinic type, and 6.) a dolichocephalic type Haddon (1929) attributed to movement of mainland Indian populations into Assam. According to Haddon, members of Risley’s (1915) mongoloid type are confined to Nepal, where those characteristics are expressed among

Gurung, Bhotia, Limbu, Murmi, and Lepcha tribal popuations. Haddon asserts that Indo-

Aryan elements were introduced to this region after the Islamic conquest of India drove groups of eastern Rajputs into the Nepalese hills, where they experienced “mongolization.” Hence, in summary, Haddon’s general racial elements of India consisted of Indo-Alpine, Indo-Afghan,

Indo-Aryan, Dravidian, pre-Dravidian, Mongolian, and Tibeto-Burmese types.

Von-Eickstedt (1934)

Von-Eickstedt (1934) proposed that four racial elements are evident among living Indians: the

Weddid, Melanid, Indid, and Paleo-mongoloid types. Von-Eickstedt (1952a, b) based his classification primarily on skin color, geographic location, and cultural characteristics (see

Figure 3). According to Von-Eickstedt, members of the Weddid type are distributed across

North and South-central India and represent the second oldest racial type on the subcontinent.

Weddid sub-types include the dark-skinned, curly black-haired Gondids and the lighter- skinned, brown-haired Malids. Von-Eickstedt (1952a, b) argued that Weddid-Gondid groups generally feature totemic cultures with matrifocal social organization. Von- Eickstedt offers the tribal Gonds and Oraons as model specimens of this type. Members of the Weddid-Malid type, on the other hand, are characterized by a lack of totemic practices, and physical features that are nearly identical to those of the Weddid-Gondid type. Von-Eickstedt (1934) argues the Malid subtype is the product of outside influence derived from culture contact between Malids,

Veddas, and Kurumbas, the latter of which he assigned to the Melanid, or “black Indian” type.

The Melanid type is composed of two sub-types: southern and northern, or Kolid. Members of the southern Melanid type are characterized by black or dark brown skin and strong matrifocal

cultural practices. Von-Eickstedt (1934) offers Yanadi tribals as a classic example of this type.

Alternately, he describes ethno-linguistic groups of the Kolid sub-type as a “primitive” people of the northern Deccan Forest that exhibit totemism and matrifocality. According to von-Eickstedt

(1952a, 1952b) the Malanids represent the earliest occupants of India and are represented by the living Mundas and Santals of Chota Nagpur. The Indid type also encompasses two subgroups: Gracile and North. Members of the southern, or Gracile Indid sub-type are characterized by brown skin and sharp, lightly-built facial features. Ethnic groups attributed to this type exhibit patrifocal cultural characteristics and are represented by living Bengalis.

Figure 3: Eickstedt’s (1952a) racial types of India.

Members of the North Indid sub-type are identified by Von Eickstedt as possessing lighter skin, and living in a patriarchal society. Von-Eickstedt (1929) offers the Rajputs and

Todas as classic examples of the northern Indid sub-type, along with most ethnolinguistic groups in far northern India and Pakistan. According to Von-Eickstedt, members of the North

Indid and Gracile Indid sub-types types, represent the living descendants of the pre-Aryan populations of the Indus Valley. Von-Eickstedt (1952b) argues that while “Aryan invasions” of

India appear to have occurred in the past, such immigrations did not have significant impacts upon local populations. The last of von-Eickstedt’s (1952a, 1952b) physical types is the Paleo- mongoloid racial stock. This type represents two temporally distinct movements of eastern populations into the Nepal and the Northern Hill States of India. Members of this recent stock are characterized by various combinations of Indic and Mongoloid characteristics, in which there is greater expression of Mongoloid characteristics found among the “primitive” sub-type, while members of the “progressive” sub-type are held to be characterized by Euro-Asiatic facial features and dark skin.

Hutton (1932)

Hutton (1932) proposed yet another model of Indian population history based on racial types. He argued that Risley’s (1915) Negrito type represents the aboriginal race of India,

Burma, and the Andaman Islands. In support of this claim, Hutton (1932) referred to the frizzy hair, short stature, and negroid facial characteristics found among the Kadar and Urali tribal populations of Cochin, South India. He referenced Guha’s (1928) anthropometric study Negrito

Racial Strains in India (see discussion below) as additional support for the indigenous Negrito hypothesis. This claim parallels Giufrida-Ruggeri’s (1917) recapitulation of De Quatrefages

(1877) assertion that an ancient Negrito race once populated the entire coastline between

Mesopotamia and India during the Neolithic. Hutton (1932) argued that the Negrito type was akmost replaced by the dolichocephalic Proto-Australoid type, surviving only in pockets of

South India, Sri Lanka, and the Andaman Islands. Hutton maintains that the Proto-Australoid element was introduced into India from the west during pre-Vedic times and offers the Veddas,

Malhavedans, Irulas, Sholagas, and members of other tribes of southern India and Sri Lanka as examples of this type. This allegedly “pure” type parallels Risley’s (1915) pure Dravidian type in its distribution, and Hutton similarly claims that Mediterranean and Negroid admixture is present in many of the outer Proto-Australoid groups.

Hutton (1932) argues that groups exhibiting the Mediterranean type entered India after the initial phase of Proto-Australoid immigration. This “racial horde,” was of West-Eurasian origin and brought with it crude agricultural technology and megalithic ritual cults. A second, more culturally refined wave of immigrating Mediterranean types allegedly arrived later from

Eastern Europe, equipped with a relatively advanced knowledge of metallurgy. Hutton (1932) was one of the first to consider the anthropometry of living populations in relation to that of archaeological populations to test hypotheses regarding the population history of South Asia.

He argued that the Mediterranean racial type is dominantly expressed in Indic populations, both in terms of culture and biology. Hutton (1932) supported these claims with reference to archaeologically derived human remains from Nal in Baluchistan, as well as archaeological remains from Sialkot and Bayana. That is, the cemetery populations of these sites exhibit dolichocephalic cranial morphology very similar to that of the ancient Mesopotamian population of Kish and the prehistoric occupants of Anau in Turkenistan. This accounts for the remnants of the proto-Australoid tribes that became sequestered in northwestern and northeastern South Asia. Hutton (1932) suggests that members of the Proto-australoid type populations had strong connections with the prehistoric inhabitants of the Indus Valley.

Hutton (1932) identified a brachycephalic leptorrhine type found primarily in present- day Pakistan and, to a lesser degree, in northeast India. With this identification, he asserted that Risley’s (1915) Scytho-Dravidian and Turko-Iranian types were invalid. Hutton’s (1932) justification for these critiques are borne out in his argument that the Sycthians, or Saka are predominantly dolichocephalic in cranial shape, thus ruling them out as ancestors of the brachycephalic type. Additionally, Risley’s (1915) so-called Turkish influence is more correctly attributed to Mongolian invasions. Previously, Chandra (1916, 1922a, 1922b) criticized Risley

(1915) for his incorrect classification of brachycephalic populations (also see Haddon 1924,

1929, Crooke 1896, Buxton 1925, Buxton and Rice 1931). Chandra (1916) argued the brachycephalic type is better attributed to non-Indian, Tocharian-speaking populations originating in the valleys of the Pamir mountains. These populations, Chandra (1916) argued, were of the Alpine type and invaded India only to be stopped by the resistance of the existing

Vedic Aryan population. Stein (1916) provided archaeological support for this assertion based on anthropometric measurements performed on skeletons from Loulan, a site located in the

Lop nor region of the Xinjiang province of Western China (see also Hemphill 2012, 2013).

According to Hutton (1932), however, members of this brachycephalic type more likely entered the northwestern periphery of India (Pakistan) long before any invasion of Vedic

Aryans. This is not to say that Hutton agreed with Giufrida Ruggeri (1917), who attributed the brachycephalic type to “stone age” peoples, for Hutton preferred Dixon’s (1922) assertion that members of this western brachycephalic type likely entered northwestern India between the

2nd and 3rd millennia B.C., long before the arrival of Vedic Aryans. Curiously, Hutton (1932) suggests that members of this brachycephalic expansion were the populations responsible either for the founding or for the destruction of Mohenjo-Daro. As such, Hutton (1932) asserts that living populations such as the Prabhus, Marathas, Kunbis, and Billavas, represent ancestors of the brachycephalic populations that were displaced from the Indus Valley and

Northwest India during the First millennium B.C. due to the invasion of Vedic Aryans.

This brachycephalic type, according to Hutton (1932), can most likely be applied to groups that speak languages belonging to either the Dardic or Pisacha branches of the Indo-

European linguistic stock. Grierson (1957) maintained that Dardic languages represent Vedic

Aryan dialects, while he believed that Pisacha languages were more closely related to those of the Pamir family. Grierson (1957) argued that Burushaski, spoken by the Burusho of Hunza and Nagar, represents the original Dardic language of northern India, while the Pamir-like

Pisacha languages represent a more recent development achieved either through borrowing or forced acquisition. Kalla (1994) points out that anthropometric studies identified individuals

Figure 4: Guha’s Racial Types: 1. Negrito, 2. Proto-Australoids and Negritos, 3. Palae-Mediterraneans and Proto-Australoids, 3a. Paleo-Mediterraneans, Proto-Australoids, and Alpino-Dinarics, 3b. Paleo-Mediterraneans, Proto-Australoids, Alpino-Dinarics, and Nordics, 3c. Proto-Australoids and Apino-Dinarics, 4. Alpino-Dinarics, Orientals, and Mediterraneans, 4a. Paleo-Mediterraneans, Mediterraneans, and Alpino-Dinarics, 5. Mediterraneans, Orientals, and Proto-Nordics, 6. Orientals and Tibeto-Mongoloids, 7. Tibeto-Mongoloids and Paleo-Mongoloids, 7a. Broad-headed Paleo-Mongoloids, 8. Paleo-Mongoloids, 8a. Paleo Mediterraneans and Paleo-Mongoloids.

with brachycephalic heads among the inhabitants of Chitral, while the Burusho exhibit mixed cranial morphological characteristics. As such, Kalla (1994) argues that the mixed anthropometric characteristics of the living Burusho likely represent admixture between members of the brachycephalic Alpine populations of the Pamirs and the Dardic, or Vedic

Aryan-speaking populations of north India and Pakistan.

Hutton (1932) concluded that the famous “Aryan Invasion of the Rig Veda” occurred circa 1,500 B.C. and is responsible for the movement of dolichocephalic populations into North

India, while Mediterranian and Armenoid populations are entirely pre-Vedic and disappeared culturally due to Indo-Aryan domination. Hutton (1932) argues it was these pre-Vedic populations that supplied the Aryans of the Rig Veda with writing and Vedic beliefs, which initiated Vedic Hinduism and a system of elite dominance maintained through the caste system. This is because philological evidence suggests that while the Rig Vedas were written east of the Ganges, they were most likely composed between the Indus and the Ganges Valleys.

Further, the Australoid tribes, today represented by such tribal groups as Bhils and Shodras5 occupied the hills and forests of North India prior to 1,500 B.C. and were little affected by the earlier intrusions of Mediterranean and brachycephalic populations (Hutton 1932).

Guha (1935, et al. 1951, 1955)

Guha (1935) and Guha and co-workers (1951, 1955) conducted research for the anthropometric survey as part of the Indian Census published between 1927 and 1955. This survey included 2511 subjects from localities throughout the Indian subcontinent. Guha

(1944, 1951, 1955) identified six regionally defined racial types and nine sub-types across

India, based on 18 cranio-facial measurements, anthroposcopic observations of skin and hair color and texture, and stature (see Figure 4). Six regionally defined racial types are Negritos,

Proto-Australoids, Mongoloids, Mediterranians, Western Brachycephals, Armenoids, and

Nordics (Figure 4).

5 The Bhils and Shodras, interestingly, are commonly viewed as being very different from one another.

The first of Guha’s (1944, 1951, 1955) racial types is that of the Negrito (Figure 4: 1), found primarily on the Andaman Islands. Controversy is associated with the presence of small pockets of Negrito populations in areas predominantly Australoid. Guha (1944) argues that the

Negrito type is not representative of the earliest Indian population. Instead, this type likely represents a specialized non-Indian population that populated the equatorial forest belt, but did not mix with their Australoid neighbors. The second type identified by Guha are Proto-

Australoids, which he suggested reflect Australian and Paupa New Guinean influences upon populations of the Indian subcontinent (figure 4:2). The third type, Mongoloids, are characterized by East Asian features, and are predominant among the populations of northeastern India. Three sub-types are identified: paleo-, dolichocephalic-, and Tibeto- mongoloids. For the current study, the greatest emphasis is placed on the Tibeto-mongoloid types, which Guha argues accounts for east-Asian features among populations in the northwestern borderlands (Pakistan). Guha based much of his work on an osteometric comparison of archaeologically derived skulls; the problem is that such data are not suitable for comparison with anthropometric measurements obtained from living individuals that are affected by the presence of soft tissue. Finally, Guha’s Alpine, Nordic, and Mediterrainian sub- types (western brachycephalics) were applied to populations in what is now Pakistan. These types, according to Guha, account for Indo-Iranian speaking Arab influences, as well as

Northern European and Central Asian influences upon populations living in the northwestern region of the sub-continent. According to Guha, these are the populations that represent the descendants of Aryan invaders (also see Kalla 1994).

Uniparental Molecular Genetic Markers

While there are many more studies that have focused on the anthropometry of South

Asia, it is important for the sake of brevity to shift focus toward the genetics of human populations currently living on the sub-continent. The study of population affinities in this region has produced a vast array of publications that are far too numerous to cover in a short chapter. Nonetheless, an attempt is made here to briefly summarize some of the most

important recent findings in South Asian population genetics. Primarily, population genetics research involving South Asian ethnic groups has focused on relationships between sub- populations based on caste affiliation, language, and geographic location. Secondarily, most of these studies attempt to determine whether local populations have experienced gene flow with populations located outside of the sub-continent.

The discussion of South Asian population genetics will begin with paternal inheritance.

Uniparental inheritance (mtDNA & Y-Chromosomal) markers exhibit several characteristics that make them useful in determining patterns of genetic affinities between human populations. These characteristics include a small effective population size, high rates of polymorphisms, and a lack of recombination. Most recently, advances in DNA extraction and analysis have allowed geneticists to answer many questions about Indian population history via hierarchical classification of Y-chromosome binary polymorphisms and cross-reference with short tandem repeat loci.

For example, Bhattacharyya and co-workers (1999) conducted Y-chromosome analysis on samples obtained from 10 ethnic groups representing eight castes (Brahmins from north,

Brahmins from east, Chamar, Bagdi, Mahishya, Agharia, Rajput and Tanti) and two tribes

(Lodha and Santal). The resulting distribution of haplotypes demonstrated that male gene flow has not occurred between living Indian sub-populations; that is, all groups considered exhibited dis-jointed haplotypes. Moreover, Hindu caste groups and tribals formed separate aggregates. No significant differences between populations were identified based on geographic location, nor were caste populations distinguished from one another via AMOVA. Ramana and coworkers (2001) conducted Y-SNP analysis6 on samples obtained from Vizag Brahmins,

Pereru Brahmins, Kammas, Bagata, Poroja, and Valmiki from Andhra Pradesh. The first three are caste groups, while the latter three represent tribes. In sharp contrast to the findings of

Bhattacharyya and coworkers (1999), Ramana and coworkers (2001) identified shared

6 SNP denotes a single nucleotide polymorphism.

haplotypes between caste and tribal populations. These researchers cite such findings as evidence of recent gene flow between castes and tribes in this region of southeastern India.

Other genetic research featuring South Asian populations has been obtained through combined uniparental and autosomal inheritance markers. Bamshad and coworkers’ (2001) analysis of Y-chromosome, mtDNA, and autosomal markers in their examination of eight South

Indian populations classified based on the basis of caste affiliation. These included three high- status castes (Niyogiand Vydiki Brahmin, Kshatriya, Vysya), two middle-status castes (Telega and Turpu Kapu, Yadava), and three low-status castes (Relli, Madiga, Mala). Their results suggest that members of the three high-status castes share closer affinities with Europeans, than with East Asians, while members of the low-status castes exhibited the opposite pattern.

Members of the middle-status castes possessed equidistant affinities to Europeans and East

Asians. The opposite pattern of affinities was demonstrated for caste populations and Africans.

That is, affinities between Indian and African populations increase relative to caste status, such that members of the lowest-status castes have closest affinities to Africans. Bamshad and coworkers (2001) interpret these results as evidence of a proto-Asian origin for all caste groups, but they maintain that subsequent West Eurasian admixture is responsible for the observed genetic differences between caste populations based on caste rank.

Kivisild and coworkers (2003) examined Y-chromosome and mtDNA markers among eight caste and tribal populations. These authors, like Bamshad and coworkers (2001), sought to test affinities between Indian populations and those of Europe, West Asia, and Central Asia.

Based on the patterning of sample data, Kivisild and coworkers (2003) asserted that the divergence between the western and eastern Eurasian gene pools occured in India during the

Pleistocene. In contrast to earlier studies, these authors found no genetic distinctions between caste and tribal populations. Three divergent Eurasian Y-chromosome lineages were identified among Indian populations. These include haplogroups C, F, and K, as well as H, L, and R2.

Kivisild and coworkers (2003) present these findings as evidence that haplogroups H, L, and R2 represent ancestral Eurasian lineages introduced to the sub-continent with the initial

colonization of India by human populations moving out of Africa. Divergence estimates, according to these authors, predict this entry to have occurred between 45,000 and 60,000 years ago.

The mechanics of this initial settlement can be described in terms of founder effect via separate migrations out of Africa, one leading northwest into Europe and west Asia and the other leading east to Central and South Asia. With such a scenario, the original Y- chromosome haplotypes (C, D, F, and K) of the founding populations of the western route were altered during the course of migration; C and D were lost, leaving F and K to serve as the founding haplotypes in West Asia and Europe. Kivisild and coworkers (2003) argue that the initial migrations via the eastern and western routes must have occurred simultaneously, and that isolation, founder effect, and genetic drift resulted in specific founding haplotype lineages for regional populations. Interestingly, in sharp contrast to other Indian populations, ethnic groups living in Punjab and the Chenchu tribals of Andhra Pradesh exhibited significantly high frequencies of the Eurasian haplotype R1a and higher variation in R1a associated STRs. Such findings led Kivisild and coworkers (2003) to posit that the R1a haplogroup has origins in both

South and West Asia. Moreover, East Asian haplogroup O does not appear to have been present in Indian populations, except among Austro-Asiatic-speaking populations. This exception was limited to the sub-aggregate haplotype M95. The general conclusion reached by

Kivisild and coworkers (2003) based on this evidence is that Indian caste and tribal populations have close affinities that developed during the Pleistocene, but recent gene flow between living groups (regardless of caste or tribal status) has also had an effect upon the genetic structure of Indian populations.

The assertion that the caste and tribal populations of South Asia share a common heritage that dates to the founding lineages of the Indian sub-continent is not accepted by all researchers. For instance, Cordaux and coworkers (2004) come to the conclusion that tribal and caste populations have independent paternal lineages. Their study included 15 tribal and

12 caste affiliated populations and was based on analysis of variation in 931 Y-chromosomes.

The results suggested that haplogroup distributions indicate divergent patterns between tribal and caste groups, regardless of social-status. Interestingly, caste groups, without reference to rank, exhibit closest affinities to each other, possess secondary affinities to Central Asians, and have negligible affinities to South Indian tribal groups. This assertion is supported by the fact that haplogroups H-M52 and F-M89 were most represented among tribals, while R-M17, J-

M172, R-M124, and L-M20 were most commonly attributed to caste groups. The infrequent appearance of H-M52 and F-M89 among caste samples and the even less frequent occurance of

M17, J-M172, R-M172, and L-M172 might be explained as a consequence of recent admixture, sampling error, or misclassification of individuals belonging to various caste and tribal samples. Because haplotype R-M17 occurs in high frequency among living Central Asians and

J-M172 is rare in West and East Asian populations, Cordaux and coworkers (2004) suggest a

Central Asian origin for this Y-chromosome marker. Hence, as suggested by Kivisild and coworkers (2003), tribal populations appear to have the highest frequency of indigenous haplotypes, but caste groups clearly possess affinities to Central Asians. From these findings,

Cordaux and coworkers (2004) conclude that caste populations are descendants of Central

Asian migrants that entered the sub-continent circa 1,500 B.C., while tribal groups appear to possess an indigenous identity. Infrequent paternal gene flow between tribal and caste groups over the last 3,500 years has resulted in low amounts of admixture between these populations.

The results of the studies mentioned above offer possible corraboratory evidence for two models for South Asian population history. The first favors indigenous development of all

Indian populations since the Pleistocene (Kivisild et al. 2003), while the second suggests an incursion of Central Asians into the sub-continent circa 1,500 B.C. followed by admixture between these groups and existing indigenous tribal populations. Nonetheless, Sengupta and coworkers (2006), conducted a combined analysis of 69 Y-chromosome binary markers and 10 microsatelitte markers, and argued based on the results that Central Asian influences are all but absent from Indian populations. That is, Central Asian immigrants and indigenous populations do not appear to have experienced admixture. In this case, 17 tribal and 18 caste

populations (and one group of Muslims) were defined based on linguistic association, caste affiliation, and geographic location. While this study offered support for Cordaux’s et al. (2004) assertion that tribal and caste groups possess independent origins, the lack of affinities between linguistically distinct tribal populations (Dravidian, Tibeto-Burmese, and Austro-

Asiatic) based on frequency distribution of microsatelitte haplotypes O2a-M95, L1-M suggests an indigenous origin for Dravidian-speaking tribals, but not for tribals who speak Austro-

Asiatic or Tibeto-Burmese languages.

The hypothesis that posits an indigenous Indian origin for Dravidian-speaking tribals was further supported by Sahoo and coworkers’ (2006) study of Y-SNP markers among 32 tribal and 45 caste-affiliated groups. The results of this study suggested that South Indian caste groups that speak Dravidian languages are also descendants of indigenous Indians.

These researchers deny any movement of genes into the subcontinent from the northwest in concert with the spread of agricultural practices. This is because their study suggested a local origin of haplotypes F* and H. Moreover, these researchers did not find any associations between the J2, L, R1a, and R1 haplotypes noted in previous studies (see, for example,

Quintana-Murci et al. 2004) and they reject the assertion that paternal South Asian lineages are foreign to the subcontinent, regardless of linguistic or caste affiliation. One exception was noted for Haplotype O, which exhibits a pattern of isolation-by-distance based on geographic proximity alone. Krithka and coworkers (2007: 389) assert that geographic patterning of O haplotypes also provides evidence of “both large-scale immigration of Tibeto-Burman speakers

(bearing O3e chromosomes) and language change of former Austro-Asiatic speakers (of O2a lineage)” in northeastern India. These studies produced results that directly refute those of

Quintana-Murci and coworkers (2004), which suggested outside origins for tribal and low- status Dravidian-speaking caste populations, which presents yet another model for the population history of South Asia. That is, Quintana-Murci and coworkers (2004) argue that

Dravidian speaking tribal and low-status caste populations have proto-Elamitic origins, a notion that stands in partial agreement with Renfrew’s (1989) hypothesis A (See models below).

Thanseem and coworkers (2006) sought to expand on these earlier studies through a contrast of mtDNA and Y-chromosome markers among Pardhan, Andh, and Naikpod tribals with other regionally defined Indian populations. The primary goal of this research was to determine whether low-status caste affiliated groups and tribals share closer affinities to one another than low-status castes share with high-status castes. In partial agreement with the work of Quintana-Murci and coworkers (2004), high-status caste groups ubiquitously possessed R*, R1, and R2 haplotypes at higher frequencies than either low-status castes or tribal populations. Low-status caste populations belonged specifically to mtDNA haplogroup H, while all major Y-chromosome lineages were found in nearly identical frequencies in low-status caste and tribal samples. This, combined with the fact that high-status caste populations exhibit unique patterns not found in the other sample groups, suggests indigenous origins for both low-status caste and tribal groups. This notion is further supported by the shared haplotypes (F*, -M89, H-M52, and O-M95) among tribals and low caste samples. Western

Asian lineages were also identified, but in contrast to the arguments of Quintana-Murci and coworkers (2004), Thanseem and coworkers (2006) assert that low frequencies of Central Asian haplotypes J2, R1, and R2 among South Indian tribal populations indicates that this signature existed in the ancestors of these populations long before the alleged arrival of Indo-European speakers, regardless whether they arrive during the 5th Millenium B.C. out of Elam, or circa

1,500 B.C. from Central Asia. Hence, according to Thanseem and coworkers, cultural and linguistic influences introduced to India by migrating populations from Central Asia or elsewhere do not appear to have been accompanied by significant amount of gene flow.

Examination of this handful of studies presents support for three different models of

South Asian population history. These may be described as 1) The Long Standing Continuity

Model, 2) The Aryan Invasion Model, and 3) The Early Entrance Model (see models and expectations below). However, it is important to note some of the limitations common to genetic research in this area. First, the scope of these studies has been fairly narrow considering that there are some 5,000 or more ethno-linguistic groups living in South Asia

today. A second problem is the lack of extensive mapping of Y-SNP and Y-STR markers. This, combined with relatively small samples sizes, use of ethnic classifications based solely on linguistic criteria and/or caste affiliation, and their limitation to contemporary populations, make these studies incomplete and as a result much of South Asian population history remains unresolved.

Chapter Five: Materials and Methods

The focal samples introduced by this study consist of 606 plaster casts of the dentition of living Wakhi (n=336) and Shina (n=270) individuals. These dental casts are curated at the

Centre for South Asian Research at California State University, Bakersfield and were collected by Dr. Brian Hemphill during two field seasons in 2007 and 2008. Wakhi samples were collected at two different locations (Gulmit n=166 [2007] and Sost n=170 [2008]), while the

Shina samples were collected primarily in Astore (n = 170 [2008]), but smaller samples were obtained during the 2007 field season from populations living in Gilgit (n=65) and Haramosh

(n=35). These samples will be considered separately to test for similarities and differences between regionally separated populations that may have shared a common origin, but who may have subsequently become isolated by harsh terrain and/or geo-political boundaries. The affinities of these two ethnolinguistic groups of Gilgit-Baltistan, Pakistan will be placed into wider perspective through comparison with 13 living and 14 prehistoric dental samples from

Central Asia, the Indus Valley, and India, for a total sample of 2802 individuals (Table 1).

This study of population affinities via odontometric comparison involves measurement of tooth diameters that are under strong genetic control. Maximum mesiodistal (MD) and buccolingual (BL) measurements were obtained with digital calipers for all permanent teeth, except for the third molars according to the methods of Moorrees (1957) and Wolpoff (1971).

Personal information collected in the field reveal that average ages among the individuals represented by these casts varies between 14 and 30 years, and average 16 years. The benefit of employing dental size data collected from adolescents is that environmental influences on tooth size and shape, such as loss, wear, and attrition, are less likely to affect the reliability of the biodistance analysis (Hemphill 1991). Individual teeth will be referred to by class and position according to the following (canines = C, incisors = I, premolars = P, Molars = M). For example, when referring to the left first mandibular molar, the abbreviation will read as follows:

LLM1. Conversely, the right maxillary second incisor will be designated as URI2. When referring to the buccolingual dimension of the lower right second premolar, the 73

Table 1: Abbreviations and Sample Sizes for Odontometric Data

designation shall be LRP2BL, whereas the designation ULCMD, refers to the mesiodistal dimension of the upper left canine.

A number of statistical analyses are necessary prior to biodistance analysis. These include measurement of intra- and inter-observer error, as well tests for sex dimorphism, asymmetry, and morphogenetic field effects (Butler 1939, Dahlberg 1945). Intra-observer error is assessed through re-measurement of 30 casts selected randomly from each Shina and

Wakhi sample, and inter-observer error is assessed through measurement of a judgment sample of casts collected from the inhabitants of Madaklasht (n=30). The thirty Madaklasht samples were chosen based upon condition to ensure the most complete measurement of inter- observer error can be assessed. Intra- and inter-observer error, and level of asymmetry between antimeres is assessed for each dimension with paired-samples t-tests. Prior to bidistance analysis, raw tooth size is scaled against the geometric mean by sample and by sex to correct for sex dimorphism and evolutionary tooth size reduction (see Jungers et al. 1995). Pairwise differences in tooth size allocation by sample are assessed with squared Euclidean distances.

The diagonal matrix of pairwise squared Euclidean distances was submitted to hierarchical cluster analysis with Ward’s (1963) method, neighbor-joining cluster analysis (Saitou and Nei

1984), multidimensional scaling with Guttman and Lingos’ coefficient of alienation (Guttman

1968), and principal coordinates analysis (Gower 1988). Statistical analyses are performed with

NTSYS, Systat 11, and Phylip-3.69.

Observer Error

Paired samples t-tests with Bonnferoni and Dunn-Sidak adjustments for measurement of intra-observer error produced reasonably consinsistant results (see Table 2, Figure 5). These measurements were taken from 35 randomly selected dental casts from the Shina (Astore) collection. Out of 28 total dimensions, only two demonstrated significant differences (LP4MD,

UP3MD). UP3MD measurements produced a p-value of 0.047 which designates a difference less than one percent greater than that expected from chance alone. This difference is not viewed as damaging the current study, for for it causes little bias upon the overall analysis of tooth-size allocation in this work. LP4MD, however, produced a significant difference (.001).

However, results of statistical analyses with or without this variable yield no differences in the patterning of samples (biodistance analysis) or in the patterning of variation among members of tooth classes examined to test morphogenetic field effects.

Further examination of intra-observer error estimates demonstrate interesting patterns that may be of use for future research (see figure 5). The most similar measurements between trials were LI2MD and UP3MD, followed by UM2MD and UM2BL. In general, the maxillary teeth were measured with greater similarity between trials than buccolingual dimensions.

Moreover, 16 of the 28 variables yield p-values in excess of 0.10. Overall, this analysis of intraobserver error indicates that the current researcher’s measurements are consistent.

Table 2: Measurement and Statistical Assessment of Intra-observer Error via Paired- Samples t-tests with Bonferroni and Dunn-Sidak adjustments Tooth & Trial 1 Trial 2 Mean SD of t p-value dimension mean mean difference difference

LM2MD 10.129 9.905 0.223 0.789 1.673 0.104

LM2BL 10.549 10.297 0.251 0.794 1.872 0.070 LM1MD 11.240 11.040 0.200 0.721 1.642 0.110 LM1BL 10.966 10.811 0.154 0.526 1.735 0.092 LP4MD 6.354 6.206 0.149 0.252 3.481 0.001

LP4BL 8.329 8.214 0.106 0.587 1.066 0.294 LP3MD 6.851 6.651 0.199 0.674 1.751 0.089 LP3BL 7.731 7.751 0.160 0.603 1.571 0.125 LCMD 6.686 6.566 0.120 0.559 1.269 0.213 LCBL 7.403 7.280 0.123 0.762 0.953 0.347 LI2MD 5.679 5.679 0 0.178 0 1.000 LI2BL 6.250 6.191 0.059 0.470 0.729 0.471 LI1MD 5.241 5.074 0.168 0.535 1.826 0.077

LI1BL 6.250 6.097 0.153 0.756 1.179 0.247 UM2MD 9.688 9.670 0.018 0.510 0.205 0.839 UM2BL 10.685 10.656 0.029 0.660 0.260 0.797 UM1MD 9.591 9.451 0.140 0.430 1.927 0.062

UM1BL 11.237 11.140 0.097 0.690 0.832 0.411 UP4MD 6.523 6.197 0.325 1.104 1.743 0.090 UP4BL 8.715 8.771 -0.056 0.392 -0.832 0.411 UP3MD 6.606 6.923 -0.317 0.909 -2.065 0.047

UP3BL 8.926 8.769 0.157 0.877 1.061 0.296 UCMD 7.569 7.377 0.191 0.608 1.863 0.071 UCBL 8.023 7.831 0.191 0.660 1.715 0.095 UI2MD 6.557 6.423 0.134 0.876 0.906 0.371 UI2BL 6.734 6.463 0.271 0.927 1.732 0.092 UI1MD 8.300 8.270 0.030 0.252 0.691 0.494 UI1BL 7.706 7.526 0.180 0.764 1.394 0.172

1.0 0.9 0.8 0.7

e 0.6

a 0.5

P 0.4 0.3 0.2 0.1 0.0

LCBLLCMDLI1BLLI1MDLI2BLLI2MD UCBL UI1BL UI2BL LM1BLLM1MDLM2BLLM2MDLP3BLLP3MDLP4BLLP4MDUCMDUI1MDUI2MDUM1BLUM1MDUM2BLUM2MDUP3BLUP3MDUP4BLUP4MD Tooth and Dimension

Figure 5: Levels of significance of intraobserver differences in measurement across trials.

Paired sample t-tests for inter-observer error were based on repeated measurement of

35 dental casts randomly selected from the Madaklasht dental series. A total of 21 of the 28 variables exhibit higher reliability for mesiodistal dimensions than for buccolingual dimensions. Only one (UM2BL) of the variables yielded a significant difference (see Table 3 &

Figure 6). This is likely due to differences between the two observers as to when a partially erupted or partially cast tooth was sufficiently preserved to be measured. Given such results, it may be stated with confidence that O’Neill measures consistently with Hemphill and therefore measurements made by these two researchers may be considered comparable.

Table 3: Measurement and Statistical Assessment of Inter-observer Error via Paired- Samples t-tests with Bonferroni and Dunn-Sidak adjustments Tooth & Trial 1 Trial 2 Mean SD of t p-value dimension mean mean difference difference

LM2MD 9.647 9.624 0.024 0.403 0.304 0.736 LM2BL 9.876 9.859 0.018 0.178 0.577 0.568 LM1MD 10.650 10.526 0.124 0.438 1.646 0.109 LM1BL 10.143 10.049 0.094 0.320 1.744 0.090 LP4MD 6.291 6.206 0.086 0.285 1.779 0.084 LP4BL 7.817 7.820 -0.003 0.216 -0.078 0.938 LP3MD 6.623 6.586 0.037 0.326 0.673 0.505 LP3BL 7.349 7.420 -0.071 0.216 -1.953 0.059 LCMD 6.391 6.260 0.131 0.374 2.079 0.045 LCBL 6.829 6.880 -0.051 0.248 -1.228 0.228 LI2MD 5.682 5.621 0.062 0.300 1.202 0.238 LI2BL 5.735 5.888 -0.153 0.484 -1.841 0.075 LI1MD 5.080 4.983 0.097 0.294 1.958 0.058 LI1BL 5.468 5.550 -0.082 0.379 -1.266 0.214 UM2MD 9.400 9.309 0.091 0.811 0.656 0.517 UM2BL 10.465 10.591 -0.126 0.251 -2.933 0.006 UM1MD 9.894 9.811 0.083 0.331 1.480 .0148 UM1BL 11.085 11.000 0.085 0.335 1.485 0.147 UP4MD 5.968 6.012 -0.044 0.243 -1.060 0.297 UP4BL 8.715 8.771 -0.056 0.392 -0.832 0.411 78

UP3MD 6.450 6.468 -0.018 0.260 -0.396 0.695 UP3BL 8.774 8.785 -0.012 0.474 -0.145 0.886 UCMD 7.250 7.203 0.047 0.256 1.071 0.292 UCBL 7.658 7.733 -0.076 0.352 -1.237 0.255 UI2MD 6.355 6.239 0.115 0.375 1.764 0.087 UI2BL 6.194 6.206 -0.012 0.340 -0.205 0.839 UI1MD 8.300 8.270 0.030 0.252 0.691 0.494 UI1BL 7.209 7.241 -0.031 0.334 -0.530 0.600

1.0 0.9 0.8 0.7

e 0.6

a 0.5

P 0.4 0.3 0.2 0.1 0.0

LCBLLCMDLI1BLLI1MDLI2BLLI2MD UCBL UI1BL UI2BL LM1BLLM1MDLM2BLLM2MDLP3BLLP3MDLP4BLLP4MDUCMDUI1MDUI2MDUM1BLUM1MDUM2BLUM2MDUP3BLUP3MDUP4BLUP4MD tooth and dimension

Figure 6: Bar graph showing levels of interobserver error.

Sex Dimorphism

Descriptive data for measurement of sex dimorphism are presented in Tables 4 and 5, as well as Figures 5 & 6. As expected, Wakhi and Shina males possess slightly larger teeth than females for most variables. This is not suprising given the fact that males tend to have larger body sizes than females, as demonstrated by numerous osteometric and anthropometric studies. Nonetheless, it is not well known that, in general, tooth-size scales with body size among humans. In fact, there does not seem to be a good correlation between body size and tooth size among humans. This is illustrated by the fact that Australian aborigines are no larger than Scandinavians in overall body size, but they possess total crown areas that far exceed those found among Scandinavians (Dr. Brian Hemphill 2013, personal communication).

This characteristic of allometric scaling is, however, well-studied in relation to the permanent canines of various anthropoids, including humans (see for discussion, Kieser 1990, see for example Wolpof 1985). Overall, sex-dimorphism is expressed to a greater degree among Wakhi samples than among Shina samples (see figure 9).

The degree of sex dimorphism is calculated as a percentage for each tooth dimension.

This measure reflects female size as it is expressed by males ((Male-Female)/Female). The percentages of sex dimorphism in Wakhi samples (Table 5) range from a low of 3.2% (LP4BL) and a high of 9.7% (UM2MD). All canines exhibit high levels of sex dimorphism between 6.4%

(LCBL) and 8.9% (LCMD). Overall, mesiodistal dimensions tend to be more sexually dimorphic, except for the upper second incisor, which exhibits the second highest percentage for this trait

(9.1%). The relatively low levels of sex dimorphism found among Wakhi highlanders may indicate that members of this ethnic population experienced high levels of nutritional stress in the past.

Table 4: Descriptive Data for Wakhi Samples Based on Sex Tooth & Sex N Mean % Sex SD Std. Error dimension Dimorphism Mean LM2MD M 50 10.128 7.0 0.694 0.098 F 66 9.418 0.553 0.068 LM2BL M 53 10.248 8.0 0.684 0.094 F 66 9.433 0.637 0.078 LM1MD M 76 10.995 7.3 0.651 0.075 F 81 10.195 0.723 0.080 LM1BL M 75 10.655 5.0 0.781 0.090 F 80 10.122 0.638 0.071 LP4MD M 65 6.695 6.2 0.698 0.087 F 74 6.280 0.470 0.055 LP4BL M 66 8.088 3.2 0.761 0.094 F 72 7.830 0.503 0.059 LP3MD M 71 6.548 5.2 0.483 0.057 F 80 6.209 0.431 0.048 LP3BL M 72 7.443 3.6 0.662 0.078 F 78 7.178 0.502 0.057 LCMD M 68 6.576 8.9 0.491 0.060 F 81 5.990 0.350 0.039 LCBL M 69 7.132 6.4 0.639 0.077 F 78 6.672 0.557 0.063 LI2MD M 73 5.598 5.2 0.450 0.053 F 80 5.306 0.366 0.041 LI2BL M 74 6.142 4.3 0.376 0.067 F 77 5.876 0.530 0.060 LI1MD M 67 5.199 7.6 0.437 0.053 F 74 4.803 0.341 0.040 LI1BL M 69 5.906 4.7 0.516 0.062 F 74 5.627 0.492 0.057 UM2MD M 48 9.779 9.7 0.548 0.079 F 57 8.830 0.674 0.089 UM2BL M 53 11.194 8.9 0.730 0.100 F 64 10.197 0.683 0.085 UM1MD M 78 10.288 5.7 0.613 0.069 F 76 9.704 0.548 0.067 UM1BL M 79 11.377 7.2 .0743 0.084 F 76 10.544 0.617 0.071 UP4MD M 70 6.268 7.8 0.910 0.109 F 74 5.779 0.730 0.085 UP4BL M 72 8.978 5.8 0.823 0.097 F 74 8.457 0.616 0.072 UP3MD M 72 6.453 4.6 0.512 0.060 F 75 6.155 0.444 0.051 UP3BL M 74 8.572 3.6 0.674 0.078 F 76 8.266 0.687 0.070 UCMD M 68 7.406 7.5 0.477 0.058 F 71 6.847 0.499 0.059 UCBL M 66 7.886 6.7 0.661 0.081 F 69 7.358 0.659 0.079 81

UI2MD M 75 6.267 7.2 0.661 0.076 F 73 5.818 0.636 0.074 UI2BL M 77 6.344 9.1 0.581 0.066 F 71 5.768 0.768 0.091 UI1MD M 80 8.298 6.4 0.582 0.065 F 75 7.770 0.551 0.064 UI1BL M 80 7.272 8.2 0.639 0.071 F 72 6.678 0.663 0.078

s 9

o 8

x 7

n 5

r e 4

LCBLLCMDLI1BLLI1MDLI2BLLI2MD UCBL UI1BL UI2BL LM1BLLM1MDLM2BLLM2MDLP3BLLP3MDLP4BLLP4MDUCMDUI1MDUI2MDUM1BLUM1MDUM2BLUM2MDUP3BLUP3MDUP4BLUP4MD Tooth and dimension

Figure 7: Bar Graph Showing Levels of Sex Dimorphism among Wakhi Samples by Tooth and Dimension.

Table 5: Descriptive Data for Shina Samples Based on Sex Tooth & Sex N Mean % Sex SD Std. Error dimension Dimorphism Mean LM2MD M 82 9.953 3.4 0.739 0.082 F 60 9.632 0.668 0.086 LM2BL M 83 10.211 4.5 0.623 0.068 F 59 9.768 0.566 0.074 LM1MD M 87 10.980 4.7 0.653 0.070 F 66 10.492 0.510 0.063 LM1BL M 87 10.669 3.0 0.569 0.061 F 65 10.392 0.510 0.063 LP4MD M 84 6.516 3.2 0.545 0.059 F 65 6.315 0.778 0.097 LP4BL M 83 8.166 3.2 0.571 0.063 F 65 7.909 0.513 0.064 LP3MD M 89 6.559 4.2 0.496 0.053 F 65 6.297 0.463 0.057 LP3BL M 89 7.508 5.2 0.624 0.066 F 65 7.140 0.465 0.058 LCMD M 86 6.519 7.3 0.439 0.047 F 60 6.078 0.400 0.052 LCBL M 86 7.238 9.1 0.701 0.076 F 61 6.636 0.532 0.068 LI2MD M 84 5.553 3.3 0.429 0.047 F 60 5.377 0.467 0.060 LI2BL M 84 6.093 6.9 0.508 0.055 F 59 5.702 0.512 0.067 LI1MD M 83 5.024 5.1 0.381 0.042 F 58 4.781 0.427 0.056 LI1BL M 76 5.885 8.1 0.592 0.068 F 55 5.444 0.506 0.068 UM2MD M 71 9.780 4.2 0.678 0.081 F 58 9.390 0.659 0.087 UM2BL M 71 10.797 5.9 0.657 0.078 F 62 10.195 0.798 0.101 UM1MD M 86 10.230 3.0 0.679 0.073 F 70 9.933 0.632 0.076 UM1BL M 88 11.084 4.6 0.665 0.071 F 70 10.594 0.635 0.076 UP4MD M 88 6.075 4.1 0.539 0.057 F 69 5.833 0.586 0.068 UP4BL M 88 8.751 2.5 0.621 0.061 F 69 8.536 0.530 0.064 UP3MD M 88 6.514 6.1 0.583 0.062 F 69 6.138 0.438 0.053 UP3BL M 88 8.688 4.3 0.638 0.068 F 69 8.333 0.546 0.066 UCMD M 88 7.370 5.1 0.455 0.048 F 65 7.011 0.430 0.053 UCBL M 86 7.887 6.0 0.607 0.065 F 64 7.439 0.606 0.076 83

UI2MD M 87 6.417 5.7 0.691 0.074 F 66 6.073 0.594 0.073 UI2BL M 86 6.365 3.6 0.692 0.075 F 65 6.603 0.742 0.092 UI1MD M 84 8.163 3.1 0.658 0.072 F 66 7.921 0.580 0.071 UI1BL M 81 7.383 4.9 0.751 0.083 F 68 7.035 0.559 0.068

m 5

a 0

-5

LCBLLCMDLI1BLLI1MDLI2BLLI2MD UCBL UI1BL UI2BL LM1BLLM1MDLM2BLLM2MDLP3BLLP3MDLP4BLLP4MDUCMDUI1MDUI2MDUM1BLUM1MDUM2BLUM2MDUP3BLUP3MDUP4BLUP4MD tooth and dimension

Figure 8: Bar Graph Showing Percentage of Sex Dimorphism among Shina Samples by Tooth and Dimension.

Similar results were obtained among the Shina (see Table 5). For Shina samples, the percentage of sex dimorphism ranges between 2.5% (UP4BL) and 9.1% (LCBL). Measurements of Shina samples yielded high levels of sex dimorphism for buccolingual measuremaents than for mesiodistal dimensions. Only nine of twenty-eight 28 variables exhibit percentages above

5%. The rather low levels of sex dimorphism in this population may signal the effects of daughter neglect (Dr. Brian Hemphill 2013, personal communication).

These results indicate that levels of dental sex dimorphism among these two highland

Pakistani samples is lower than average levels of this phenomenon among most human populations and non-human primates (Keiser 1990, see also Plavcan 2002, 2004 ). Running counter to expectations, lower levels of sex dimorphism are found among the higher status

Shina-speaking groups than among the Wakhi, who as a group have occupied a lower social status among highland Pakistani ethnic groups over the past 100 years.

m 5

a 0

p WAKHI -5 SHIN

LCBLLCMDLI1BLLI1MDLI2BLLI2MD LP3L UCBL UI1BL UI2BL LM1BLLM1MDLM2BLLM2MDLP3MDLP4BLLP4MDUCMDUI1MDUI2MDUM1BLUM1MDUM2BLUM2MDUP3BLUP3MDUP4BLUP4MD tooth and dimension

Figure 9: Bar Graph Showing Percentage of Sex Dimorphism among Shina and Wakhi Samples by Tooth and Dimension.

Asymmetry 85

`Levels of directional asymmetry were obtained via paired t-tests with Bonfferoni and

Dun-Sidak adjustments (Tables 6 and 7). The results of these tests indicate that members of the right and left dental arcade exhibit near identical sizes. Only three dimensions exhibit significant differences between antimeres (LP3BL, LCBL, UCBL) among the Shina samples, and two

Table 6: Results of paired t-tests for Side Differences among the Wakhi

Tooth & N T DF 2-tailed dimension Significance pairs LM2MD 80 -2.284 79 0.025 LM2BL 86 -0.898 85 0.372 LM1MD 136 1.111 135 0.269 LM1BL 116 -1.749 115 0.083 LP4MD 115 0.087 114 0.931 LP4BL 108 -1.398 107 0.165 LP3MD 131 0.628 130 0.531 LP3BL 126 -2.522 125 0.013 LCMD 130 -0.146 129 0.884 LCBL 131 -0.555 130 0.580 LI2MD 129 0.377 128 0.857 LI2BL 135 0.480 134 0.923 LI1MD 120 2.083 119 0.050 LI1BL 134 0.532 133 0.595 UM2MD 60 0.447 59 0.657 UM2BL 80 -0.623 79 0.535 UM1MD 139 0.645 138 0.520 UM1BL 141 1.375 140 0.171 UP4MD 130 1.290 129 0.678 UP4BL 135 -0.693 134 0.490 UP3MD 142 0.456 141 0.649 UP3BL 145 -1.172 144 0.243 UCMD 115 1.742 114 0.084 UCBL 119 -0.326 118 0.745 UI2MD 136 0.739 135 0.461 UI2BL 131 1.689 130 0.094 UI1MD 142 -1.684 141 0.094 UI1BL 144 0.393 143 0.814

Table 7: Results of paired t-tests for Side Differences among the Shina

Tooth & N T DF 2-tailed dimension Significance pairs LM2MD 92 -1.511 91 0.134 LM2BL 92 -1.028 91 0.307 86

LM1MD 142 0.917 141 0.361 LM1BL 138 -0.887 137 0.377 LP4MD 67 1.441 66 0.154 LP4BL 137 1.150 136 0.252 LP3MD 114 1.381 113 0.170 LP3BL 102 -2.766 101 0.007 LCMD 132 0.745 131 0.457 LCBL 128 -3.038 127 0.003 LI2MD 125 1.863 124 0.065 LI2BL 120 0.587 119 0.559 LI1MD 129 1.071 128 0.286 LI1BL 119 0.162 118 0.872 UM2MD 67 -1.964 66 0.054 UM2BL 66 -2.042 65 0.045 UM1MD 148 -0.707 147 0.481 UM1BL 149 -1.972 148 0.050 UP4MD 144 -0.343 143 0.730 UP4BL 146 0.851 145 0.396 UP3MD 153 1.322 152 0.188 UP3BL 151 -0.042 150 0.967 UCMD 142 1.148 141 0.253 UCBL 132 -3.872 131 0.000 UI2MD 137 -0.314 136 0.739 UI2BL 128 0.893 127 0.374 UI1MD 146 -0.653 145 .0514 UI1BL 141 -0.079 140 0.937

dimensions exhibit such differences among the Wakhi (LM2MD, LP3BL). The fact that four of five of these variables are buccolingual breadths suggests that buccolingual dimensions likely exhibit more plasticity during dental development, or are more heavily affected by bouts of environmental stress than are mesio-distal dimensions. Overall, the low occurance of directional asymmetry among these samples is not likely to influence the outcome of population distance analysis. More importantly, the fact that most of these measures demonstrate near identical sizes between antimeric pairs allows replacement of missing values with data obtained from the appropriate antimere.

Comparative Samples

The comparative samples used in this study consist of previously published data derived from Central Asia, Pakistan, and India (Table 1, Figure 10).

Figure 10: Locations of Collection of Odontometric Samples Living South Indians

South Indian samples of two caste groups, the Pakanati Reddis (n=184) and

Gompadhompti Madigas (n=177), and one group of non-caste Hindu tribals, the Chenchu

(n=196) were analyzed by Hemphill (1991a). These samples were divided by caste and sub- caste to increase reliability of biological comparisons between three highly endogamous populations. All of these groups speak Telugu, a member of the Dravidian family of languages and inhabit districts in southern and central Andrha Pradesh, in southeastern India.

The Pakanati Reddis, also known as the Kapu, represent a high-status caste group.

While the Reddis are considered to be of higher social status than Brahmins today, they occupied a position of lower status in Indian society in the past. Higher status was attained by members of the Reddi caste recently through the acquisition of land (Dr. Chandra Reddy, 88

Director of the Archaeological Survey of India, Andrha Pradesh division, personal communication, 2009). This contention is supported by von Fürer-Haimendorf’s (1982) ethnohistoric survey, which found the Reddis of Andhra Pradesh were less affected than many other groups by European colonialism, and as a result were allowed to retain much of their land. This is how some Reddis of Andhra Pradesh became richer than Reddis residing elsewhere in India and subsequently obtained higher social-status than their mid-status cousins in other parts of India. Thurston (1909) noted that local Indian history and oral tradition identifies the ancestral Reddis as a very large and powerful tribe of Dravidian- speaking cultivators who expanded into Andrha Pradesh approximately 1600 years ago. As such, many of the Reddi sub-castes claim their ancestors were members of the Rajput caste of northwestern India. (Thurston 1909, Reddy 1982). Living primarily within nuclear family units, the Reddis practice patrilineality and patrilocality (Hemphill 1991a, von Furer-Haimendorf

1982).

Biological information concerning Reddis is lacking in general. Sethuraman and coworkers (1982) compared Pakanati blood samples with those of Sheik muslims and low- status caste Haijirans and found no differences between any of the three groups based on MN blood group frequencies. Reddy and Reddy (1977) conducted research that sought to identify patterns of fertility associated with ABO blood groups among Pakanati and Pedakanti Reddi sub-castes and other castes populations of Andhra Pradesh. This study identified close affinities among the castes, except for the Pedakanti, who were classified as outliers in relation to all other local populations. A similar study was conducted by Reddy and Subhashimi

(1984), who identified close affinities between Pakanati Reddis and the Nagava, Sam, Yanadi, and Vaisya based on ABO blood type allele polymorphism frequencies. That is, they share closest affinities with other middle-status caste groups, particularly in Andhra Pradesh.

Hemphill (1991a) argues that within-caste relationships based on analysis of blood group distributions suggests longer tenure in Andhra Pradesh for the middle-status castes than for high-status Brahmins.

Reddy and coworkers (1987) conducted anthropomorphic research on five Reddi sub- caste populations that encompassed 750 male participants, 15 measurements, and calculation of 10 indices. They found that the Pakanati and three other sub-castes were marked by similar expression of these anthropomorphic characteristics, but that the Pokanati sub-caste did not fit into the same metric catagories. All but one Reddi sub-caste group exhibited mesocephaly; the Pokanati exhibited dolicocephaly (Reddy and Reddy 1987). Perhaps counterintuitively, the

Pokanati Reddis demonstrated the highest mean measurements for all dimensions considered, except bicristal breadth. This is somewhat surprising, given that the Pokanati have the lowest status among the five Reddi groups considered.

Reddy (2006) examined dermal ridges among five sub-caste groups of Reddis: Pokanati,

Panta, Pedakanti, Akuthota, and Palle. Reddy’s (2006) samples were collected in southern

Andhra Pradesh, the same region from which Hemphill’s (1991a) Pakanati sample originates.

Reddy (2006) argues that rural populations, such as those represented by Hemphill’s (1991a) sample of Pakanati Reddis, strictly adhere to caste-determined endogamy. Hence, inbreeding estimates among Reddi populations range between 4% and 37%. Cluster analysis identified two aggregates based on dermal ridges. The first contained the Pokanati and Palle, while the second contained the Pedikanti, Panta, and Akuthota. The Pedakanti exhibited distinct patterns within the second aggregate. Overall, the Pedakanti were the most divergent (Reddy

2006). A recent examination of dental fluctuating asymmetry among Pakistani highlanders and South Indians identified high levels of fluctuating asymmetry among Pakanati Reddis and the highly endogamous inhabitants of Madak Lasht village located in the Hindu Kush highlands of Pakistan. Only the Chenchu tribals of Andrha Pradesh exhibited higher levels of asymmetry than the Pakanati among the Indian samples (O’Neill and Hemphill 2009a). These results confirm Hemphill’s (1991a) analysis of dental asymmetry among South Indians, which not surprisingly, also identified the highest levels of asymmetry among the Chenchu, followed by the Pakanati Reddis. Interestingly, lowest levels of asymmetry were not identified among the highest castes, as might be expected if physical and dietary disruption lead to the

developmental stress that results in expression of bilateral asymmetry. As such, O’Neill and

Hemphill (2008) concluded that high levels of asymmetry among the Chenchu, the inhabitants of Madak Lasht, and Pakanati Reddis are more the consequence of strict endogamy and high levels of inbreeding than physiological disruptions due to insufficient nutrition or disease.

The Gompadhompti Madigas represent a sub-caste of a very low-status caste. Madigas are distributed throughout India, including Rajasthan, Gujarat, Maharashtra, and Orissa. Like the low-status caste Chakkiliyans of Karnataka, Kerala, and Tamil Nadu, the Madiga are known for their leatherworking and have been described as a “caste of the left hand” (Thurston

1909, Hemphill 2009a). Moreover, the Madigas are considered untouchables by all other castes and frequently compete with Chenchu tribals over access to subsistence resources or market commodities. As low-status caste members, Madigas who are unable to engage in leatherwork are forced into wage labor or self-employment that provides meager, if any, support. Thurston

(1909) argued that Madigas, as meat-eaters and handlers of the dead, carry with them a sense of taboo among other castes, so that even very low castes such as the Mala will have nothing to do with them.

Madigas, like Reddis, have endogamous marriage restrictions and patrilineal, patrilocal kinship customs. Thurston (1909) noted that divorce and polygamy were common among

Madiga groups. Consanguineous marriages, like those found among Pakanati Reddis, follows a pattern of cross-cousin marriage. In addition to the Gompadhompti, five additional sub-castes of Madigas reside in Andhra Pradesh. These include the Ginnadhompti, Bhumidhompti,

Chatladhompti, Sibbidhompti, and Chadrapadhompti. Unrelated individuals rarely marry one another and marriage between members of different sub-castes is also quite uncommon

(Hemphill 1991a).

The biological characteristics and population affinities of the Madiga have been the focus of few studies. Reddy and coworkers (1980) and Reddy (1981) examined ABO blood groups among Madigas living in Hyderbad District, Andhra Pradesh. Cluster analysis identified close associations between Madigas and Malas, the lowest ranking of the twelve

castes considered (see also Kalla 1994, and Shaw et al. 2011). Hemphill (1991a) argues that

Madigas likely represent a tribal population that were brought into the caste system as untouchables through the process of “Hinduization” described by Bailey (1960) and Stein

(1969). This may imply Australoid origins for the lowest castes (Hemphill 1991a). Kumar and

Reddy (2003) included Madigas in their anthropomorphic studies. Cluster analysis did not identify any specific relationships between Madigas and the other samples considered.

However, Madigas consistently possessed closer affinities to the Dravidian-speaking South

Indian populations than to Mundic-speaking populations of Chota Nagpur or to North Indians, regardless of caste (see also Ghupta et al. 1960). Several genetic analyses have included

Madigas. For example, Bamshad and coworkers’ (2001a) mtDNA analysis identified close affinities between Madigas and Malas, coupled with secondary affinities between those two groups and the Relli, another relatively low-status caste. Neighbor-joining analysis demonstrated separate aggregates for low- versus high-status caste populations. Tribal populations (Kapu, Yadava) demonstrated closer affinities to low-status castes (Madiga, Mala,

Relli), than to high-status castes (Brahmin, Vysya and kyshatria). O’Neill and Hemphill’s

(2009a) study of fluctuating dental asymmetry identified the lowest levels of stress induced perturbation of dental growth among the Madigas, who demonstrated similarities to the inhabitants of the Swat Valley of Pakistan in this regard. Such results may be indicative of low levels or even an absence of inbreeding in these populations (also see Hemphill 1991a).

The last of Hemphill’s (1991) samples represent a group of non-caste Hindu tribals. Like the Madigas and the Reddis, Chenchu tribals speak Telugu, a language that belongs to the

Dravidian language family. Chenchus associate with one another based on clan membership and lineage and practice exogamous intra-clan marriage7. Clans are composed of patrilineal, patrilocal lineages and residence patterns. Inhabitants of clan villages consist of related men and their unrelated wives. Overall, villages consist of extended family units, with individual

7 This means Chenchus marry others who live outside of their birth villages, but always within their clan of birth. Therefore, they are endogamous by clan, but exogamous by village.

households maintained by patrilineal, patrilocal nuclear family units (Thurston 1909, von

Furer-Heimendorf 1943, Hemphill 1991a). The Chenchus, unlike the Madigas, practice monogamy; however on occasions a man can marry his wife’s sister as an extra wife. As with other Telugu-speaking populations of Andhra Pradesh, the Chenchu attempt to marry their cross-cousins if possible, but non-cousin marriage cannot be avoided.8 Hence, levels of inbreeding among the Chenchus are lower than average for Andhra Pradesh groups (Sirajuddin

1984). This is an unexpected result given the high levels of dental bilateral asymmetry detected among Chenchus by Hemphill (1991a) and O’Neill and Hemphill (2009a). Sirajuddin (1984) argues that high levels of inbreeding in other Andhra Pradesh populations have been driven by caste restrictions. This could also be attributed to high levels of poverty among such groups that lead to systematic stress due to malnutrition and exposure.

The biological characteristics and genetic affinities of the Chenchus are relatively well known, but their origins remain a mystery. Analysis of blood groups identifies close affinities between the Chenchu and other South Indian groups regardless of caste (Reddy 1943,

MacFarlane and Sarkar 1941, Simmons 1953, Sirajuddin 1977, Ramesh et al. 1980). For instance, Reshmi and coworkers (2002) recently identified racial differences between southern

Chenchu and central Indian Lambada tribals based on distribution of ABO and Rh(d) blood type alleles. While the Chenchu exhibit closer affinities to South Indian populations than to non-Indian populations, they consistently exhibit extreme divergence from other South Indians when local populations are considered. For example, Mittal et al. (2008) examined blood work among South Indian tribal groups and found that limited heterogeneity exists between South

Indian caste and tribal groups. While AMOVA analysis and multidimensional scaling indicated no significant patterning of affinities among South Indian groups, the Chenchus exhibited extreme differences when compared to other South Indian populations. Mittal and coworkers

8 It should be mentioned here that apart from cross-cousin marriages, maternal uncle-neice marriages are common among many groups in South India.

(2008) argue this pattern may be reflective of a bottleneck event that affected the Chenchu gene pool in the past.

Living Northwest Indians

Samples from northwest India were collected from three ethno-linguistic populations in

Gujurat and published by Lukacs and Hemphill (1993). These groups, the Vaghella Rajputs

(n=190), Garasias (n=207), and Bhils (n=208) all speak Indo-Aryan languages. The Vaghella

Rajputs represent high-status Kshatryas, the Garasias are a low-status caste, and the Bhils are non-caste tribals.

The Bhils represent a very large tribal group widely distributed across the northern and central regions of India. Their tribal name is derived from a Dravidian word, vil, meaning bow and arrow (Kumar and Bhasin 2011). Today, the largest concentrations of Bhils live in Gujurat,

Maharashtra, Rajasthan, and Madhya Pradesh (Chaudhari and Kumar 1976, Mann 1978, Man and Man 1991, Lukacs and Hemphill 1993). Like most Indian tribal groups, the Bhils practice patrilineal kinship and patrilocal residency. Nuclear families make up the primary units of

Bhil society and consanguineous marriages are taboo. Historical and ethnographic sources identify the Bhils as indigenes and former rulers of the region they inhabit today (Tod 1881,

Shaw 1942, Naik 1956, Dave 1960, Chandra 1975, Vyas 1978, Lal 1979). Linguistic evidence supports such claims, for Grierson (1908) demonstrated that the Bhili language belongs within the Indo-European linguistic family, while Fairservis and Southworth (1989) note the presence of numerous Dravidian and Mundic archaisms in the language.

Majumdar and Sen (1949) and Haque (1988) collected skeletal metric data from

Gujuratis, with a focus on the Bhils and Garasias. These researchers were unable to identify significant relationships between the Bhils and other ethnolinguistic groups of the region. The same conclusions were drawn from seriological analyses (Majumdar and Kishen 1949,

Mukherjee et al. 1979, Papiha et al. 1978, Sanghvi 1978, Vyas et al. 1962). For example,

Macfarlane and Sarkar (1941) examined ABO blood groups among 12 tribal populations in

India. Close affinities were demonstrated between the Bhil and the Korku. The Bhils, like the

Maria Gonds, a tribal group from eastern Maharashtra, and the Oraons, exhibited increased frequencies of B and AB blood types that set these tribes apart from the other eight tribes

(Macfarlane and Sarkar 1941). Shankarkumar and coworkers (1999) examined HLA and B antigens among the Bhils and the Pawras of Maharashtra. HLA, A1, A10, B5, B8, B15, and

B27 antigens were most frequent among the Bhils, while the Pawars exhibited higher proportions of HLA AII, A28, B7, B12, B35, B37, and B55 antigens. Positive linkage disequilibrium identified a common haplotype A9-B13 shared between members of the two tribes. According to Shankarkumar and coworkers (1999), this sets the Bhils and Pawras apart from other Indian tribal groups. Overall, biological and linguistic studies suggest that the Bhils likely originate from a very large population of tribals that were once widespread across the

Indian subcontinent (Lukacs and Hemphill 1993, see also Kumar and Bhasin 2003, Bhasin and Bhasin 1999, Bhasin 2005, 2009).

The second of Lukacs and Hemphill’s (1993) samples is the Garasias, a low-status

Hindu caste group that speaks an Indo-Aryan language. According to Kumar and Bhasin

(2003), their name designates them as “people of the hills" or “forest dwellers” (85, see also

Meharda 1985). Rajputs claim that “Garasia” means “degraded people.” During the 8th century

A.D., the King of Jaipur was defeated by the Turks and forced to flee into the hilly regions occupied by Bhil tribals. Although the Bhils put up a fight, the Rajputs conquered them and initiated a mode of squatting to control acquisition of land. Land plots were known to the

Rajputs as “gras” and were later extended to Bhils as “garasias,” which were basically subsistence grants, in order to pacify the resistant tribal leaders (Census of India 1891,

Hemphill 1993). Hindu caste marriage restrictions are implied but not generally followed by the

Garasia cultivators, and contract weddings are common. Most Garasia work today as wage laborers if they do not own land for farming. According to Kumar and Bhasin (2003), “[the]

Rajputana Gazetteer [states that] the Garasias are the ‘halfbreed’ […] descendants of Rajputs who married the Bhils. The settlement pattern, use of bow and arrow and the general way of life of Garasia are all similar to those of Bhils” (85). This suggests that Bhil grant holders

became the modern Garasia, once again suggesting a relationship between low caste status and tribal heritage. In support of these notions, Kumar and Bhasin (2003) demonstrated, based on 12 sero-genetic marker systems, that the Bhils and Garasias exhibit closer relations to each other than to any of the other samples. The exceptions to this pattern consisted of closest affinities between the Bhils and the Damor, who also trace their heritage to the Rajputs, and between the Garasias and Kothoti tribals who recently immigrated to Rajasthan (Kumar and Bhasin 2003).

The third of Lukacs and Hemphill’s (1993) samples are the Indo-Aryan speaking Vagella

Rajputs, a high-status caste group. The Rajputs were a powerful warrior caste that was affected by Mughal expansion during the eighth century A.D. As such, groups of Rajputs moved into adjacent areas and conquered the local residents. Serological studies have identified close affinities between Rajputs and Brahmins (Pattanyak 2006). Chahal and coworkers (2008), for example, examined blood samples from 3,222 individuals from three living high-caste populations (Brahmin, Rajput, Shilpkar) and two geographically distinct populations of Bhotia tribals in Uttarkhand. Cluster analysis of genetic distances demonstrated significant and obvious differences between the tribal and caste populations.

This difference was less marked for the Shilpkar than for the Rajputs and Brahmins.

Brahmins and Rajputs shared closest affinities among samples and demonstrated secondary affinities to the Shilpkar.

Prehistoric Indus Valley

Prehistoric Indus Valley samples were recovered from four archaeological sites located in Pakistan. These include samples from Neolithic (n= 42) and Chalcolithic (n= 28) levels at

Mehrgarh, Harappa (n= 26), Timargarha (n= 21), and Sarai Khola (n= 25). These collections are curated in museums and research collections in Pakistan. The antiquity of the prehistoric

Indus Valley samples ranges between 200 and 6,000 B.C.

The earliest archaeologically derived samples from the Indus Valley were collected from the famous site of Merhgarh, located on the west bank of the Bolan River at the edge of the

Kachi Plain of Baluchistan. These samples represent the aceramic Neolithic occupants of the site. Estimation of antiquity was achieved via cross-dating, which suggested that the earliest component at Merhgarh is at least 8000 years old. Hence the earliest levels have been identified as the early Neolithic (MR3) layers and the second earliest levels belong to periods I,

II, and III (MR3, MR2, MR). Periods IV-VII date to the Chalcolithic period (Jarrige and

Lechvallier 1979, Jarrige and Meadow 1980, Jarrige and Lechevallier 1980, Jarrige 1981,

1985). Archaeological evidence indicates that during the early Neolithic, the inhabitants of

Merhgarh were hunter-gatherers who lacked storage technology and lived a semi-sedentery lifestyle. The development of semi-sedentery lifestyle out of what may have been a pure hunter- gatherer strategy during this period may have been due to the discovery or diffusion of cultivating techniques and crude wheat and barley cultivation technology between Periods I and III. Ceramic technology, cultivation (barley and wheat), and animal husbandry (cattle, goats, sheep) all developed during the Neolithic at Merhgarh prior to 3,500 B.C. Importantly,

Mehrgarh occupied a central position along the route between the Indus Valley and West Asia

(Lechevallier and Quivron 1981, Meadow 1981, 1983, 1984, Costantini 1984, Lukacs 1986,

Dani 2007).

Abundant skeletal materials have been excavated and collected from the Neolithic levels of Merhgarh. These interments exhibited mortuary features that place the advent of ceremonial burial practices in this region between 6,000 and 4,000 B.C. A rich array of burial accoutrements was found accompanying the Neolithic burials of Merhgarh. Such grave wealth includes lapis lazuli and turquois jewelry, bitumen-lined basketry, flint blades and tools, and stone axes. During the burial process, most of the deceased individuals were placed on their left side with the elbows and knees flexed; twin and group interments rarely occurred within the cemetery boundaries. Burial orientation was predominantly expressed with head to the north, facing east toward a crudely constructed, unbaked mud brick wall. A number of animal burials were found in association with the graves. The animal burials have been identified as goat sacrifices, which suggests religious rituals may have been associated with interment of the

dead at Merhgarh (Lechivallier et al. 1982, Lechivallier and Quivron 1981, Lukacs 1986, Dani

2007). The dental samples that represent the Neolithic occupants of Merhgarh are derived from burials interred in a formal graveyard. These dental remains were first analyzed by Lukacs

(1986, 1988) and Lukacs and coworkers (1986a,b).

Lukacs (1986) examined dental morphometric patterns among the same samples from the MR3 cemetery of Merhgarh and compared them to several living and archaeological populations in India. Lukacs (1986) identified close similarities between the Neolithic inhabitants of Merhgarh, the prehistoric inhabitants of Inamgoan (located in west-central

India), and living Jats. No affinities were identified between living North Indians and the

Neolithic occupants of Merhgarh, or between the latter and living Near Eastern populations

(Lukacs 1986). This could suggest a population movement of Neolithic northern Indian populations into central Pakistan. Lukacs rejected this notion because of the apparent lack of affinities between living North Indians and the Neolithic inhabitants of Mehrgarh. However, this rejection is complicated by the association between the living Jats and the latter population.

The second oldest sample from the Indus Valley was recovered from the Chalcolithic levels at Mehrgarh. The Chalcolithic period exhibits increased technological innovation in farm tools and structures over time, as well as pottery types indicative of more refined techiniques.

The Harappan samples are derived from the famous urban center that dates between 2,300 and 1,700 B.C. (the mature Harrappan phase). It is during this period that the archaeological record reflects increased urbanization, significant changes in mortuary style, advances in irrigation technology, and the development of trade networks. The skeletons from Timargarha

(1,400 – 850 B.C.) have been attributed to the Gandharan Grave Complex. Dani (1983) claims that this archaeological population represents descendants of Central Asian invaders. Finally,

Sarai Khola, known for stylized ground stone axes, is the latest of the Indus Valley samples and posseses an antiquity between 200 – 100 B.C. One more archaeological sample, Inamgaon must be mentioned. This prehistoric sample from India was collected from the post-Harappan site of Inamgaon (n=38) in Maharashtra state. While it is located away from the Indus Valley, in

west-central India, Inamgaon exhibited many similarities to Indus Valley sites and the skeletal samples obtained there dates between 1,600 and 1,700 B.C. (Lukacs and Hemphill 1991,

Lukacs 1992, Lukacs 1983, Lukacs 1983,1987, Lukacs and Hemphill 1990, Dani 1966, 1967,

1980, Bernhard 1967, 1969, 1981, Halim 1968, 1970-71, 1972, Dhavalikar 1979, Sankalia et al. 1973, 1975, 1984, Lukacs, 1985, 1987, Lukacs et al., 1986, Lukacs and Walimbe 1986).

Prehistoric Central Asia

The Central Asian samples used in this study are derived from four prehistoric sites.

These consist of two archaeological samples from southern Uzbekistan 1) Djarkutan and

Sapalli Tepe, located in Southern Uzbekistan and 2) two samples from archaeological sites located in Turkmenistan, Altyn Depe and Geoksyur. Altyn Depe (c. 2,500 B.C.) and Geoksyur

(c. 3,500 B.C.) are the oldest of these samples, and predate the archaeological horizon that defines the sites of Djarkutan and Sapalli tepe. The latter two sites belong to the Bactrian-

Margianan Archaeological Complex (BMAC). The samples from Djarkutan are divided into three temporal phases by periods (Djarkutan, 2,000 – 1,800 B.C.; Kuzali 1,800 – 1,650 B.C.; and

Molali 1650 – 1500 B.C.). Heibert (1994) asserts that these are distinct periods due to obvious changes in material culture identified via archaeological analysis. The second group of BMAC samples are derived from skeletons excavated at Sapalli Tepe (2,200 – 2,000 B.C.).

Samples collected from Altyn Depe and Geoksyur represent the oldest Central Asian samples considered in this study; Kohl (1981) documents the development of distinct patterns of mortuary behavior, domestication of plants and animals, symbolism, and social stratification at Altyn Depe between 2,500 – 2,000 B.C. Finally, Geoksyur provided the earliest samples

(3,600 – 3,500 B.C.) and consists of an ancient village complex located in the oases that once existed in the Tedjen River delta of southeastern Turkmenistan. Nesbit and O’Hara (2000) assert that Geoksyur was a thriving city center that marked a crucial location for trade routes between Central Iran and civilizations to the east during the Namazga III period (for further discussion of these Central Asian archaeological sites, see Francfort 1994, Hiebert 1994, Kohl

1985, Lamberg-Karlovsky 1994a, 1994b, Hemphill 1998, 1999).

Living Pakistani Highlanders

Living Pakistani highlanders are represented by eight ethno-linguistic groups. These include the Wakhi of Sost and Gulmit, the Kho, the inhabitants of Madak Lasht, the Swati of

Mansehra District, and Shina speakers from Gilgit, Haramosh, and Astore. The Kho are an

Indo-Aryan speaking group that live in the Chitral District of northern Pakistan. The Kho maintain they are the descendants whose ancestors were immigrants from Central Asia, who immigrated to Pakistan during the 13th century A.D. (Hemphill 2012). The Madaklasht are an ethnolinguistic group defined by residence in Madak Lasht, an isolated village located in the remote and rugged mountains of Chitral. This group, unlike the Kho, does not speak a language that has been classified within the Dardic branch of Indo-Aryan languages, but instead they speak Dari, an Indo-Iranian language. The Swati consider themselves “Swati

Pathans,”but their Pathan neighbors deny any implied shared ancestry. Schofield (2003) argues that the Swati are descendants of Pashtun-speaking populations that were driven from the Swat Valley into the Hazara hills during the between A.D. 1500 and 1700. Ibbetson (1916) argued that the Swati have roots in peninsular India based on cultural characteristics that imply a Hindu background. Finally, three regional samples represent Shina-speaking populations. These populations speak different dialects of Shina, Gilgiti (Gilgit, Haramosh) and

Astorei (Astore). As noted earlier, these Shina-speakers do not consider themselves part of the same ethnic group, but instead trace their ethnicity based on village membership and region of birth. Most Shina-speakers do not consider themselves anscestors of the “Shin tribes” mentioned by Biddulf (1880).

100

Chapter Six: Models and Expectations

Nettle and Harris (2003) demonstrated that genetic relatedness and linguistic similarity are strongly correlated among populations of west Asia and Europe (see also Cavalli-Sforza

1988, Ruhlen 1987, Barrantes et al.1990, Barbujani and Sokal 1990, Bateman et al.1990). It has long been suggested that the same relationship exists among the populations of India,

Pakistan, and western Central Asia. This line of thought has its origins in the philological and linguistic research of such famous scholars as Sir William Jones (1786), Francis White Ellis

(1816), Bishop Robert Caldwell (1856), and Max Müller (1888). Müller (1888) proposed a scientific approach to the study of mythology and language that carried with it many of the premises of the school of racial typology that developed during the early 20th century. Müller

(1888) popularized the notion that the “Arya” of the Hindu Rig Veda invaded India during the

2nd millennium B.C. and encountered the indigenous “noseless Dasus,” whom they conquered and dominated through a system of elite dominance that resulted in the modern structure of the Indian caste system. Müller (1888) asserted that language is a better indicator of “race” than skin color. This view was partially based on observation of the geographic distribution of

South Asian languages, which features predominantly Indo-Aryan languages in the north and

Dravidian languages in the south. Müller (1888) further argued that comparative philology demonstrated that Indo-European mythologies differ according to geographic and linguistic associations.

As demonstrated previously, Müller’s (1888) Aryan invasion was perpetuated in early anthroposcopic and anthropometric studies that sought to identify racial types among the various living caste groups and tribal populations of India (Ujfalvy 1884, 1886, Flower 1885,

Waddell 1900, deTerra 1905, Thurston 1909, Risley 1915, Giufrida-Ruggeri 1917, Stein 1933,

1929, Haddon 1924, 1929, Hutton 1932, Eickstedt 1934, Guha 1935, Mahalinobis et al.1949,

Karve and Dandekar 1951, Majumdar and Rao 1960). These early bioanthropological researchers largely ignored Müller’s (1888) final lectures at the Gifford Hall, during which he

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argued that racial and linguistic types are not intrinsically related. For example, Risley (1915) identified seven racial types and argued from the basis of skin pigmentation, cranio-facial measurements, and stature that living populations in North India that speak languages attributable to the Indo-Aryan branch of the Indo-European linguistic family represent descendants of two waves of West Eurasian Aryans. The first of these waves consisted of the famous Vedic Aryans, whose entrance impacted the distribution of languages and racial types across India. The second consisted of a series of population movements from Central Asia into

South Asia during the medieval and post-medieval eras of Indian history, which roughly encompass a 500 year period between A.D. 1100 – 1600. The notion of Bronze and Iron Age

Aryan invasions of India has been criticized on many levels; nevertheless this model continues to be taught to history students in the United States and other western nations today. This is because linguistic, philological, archaeological, and cultural-historical evidence appears to support such a notion. One of the problems with the Aryan Invasion Theory is that it necessitates a movement of outsiders into India, who brought with them technological innovations and Vedic culture. Such criteria designates the indigenous populations of prehistoric India as primitive and “non-civilized,” a notion that carries with it the biases of 19th

Century western scholars and the presumably outdated ethnocentrism that riddled early anthropological investigations in South Asia.

This chapter outlines the arguments made in support of the Aryan Invasion hypothesis and its criticisms, as well as three alternative models that have been proposed for the population history of South Asia. These additional models are designated 1.) The Long

Standing Continuity Model (Kennedy 1990, Willis 2010), 2.) The Early Entrance Model (Renfrew

1984, Hemphill and Mallory 2008), and 3.) The Historic Interactions model (Blaylock 2008,

O’Neill and Hemphill 2010, Hemphill et al. 2007, 2012). Models provide the basis for the scientific study of biological populations, but these four models have been considered non- inclusive and all-exclusive in previous studies (see for instance Blaylock 2008 and Willis 2009).

The problem here is that different models may apply for the different sub-populatios considered

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in this study. This lack of dynamic interpretation of these four models will be eliminated here, as the assumptions will no longer insist that only one model can be supported.9

The Aryan Invasion Model

Advocates of the Aryan Invasion Model (AIM) assert that the presence of Indo-Aryan languages in South Asia is due to the actual physical migration of Central Asian populations who entered the northwestern region of Indian subcontinent during the first half of the 2nd millennium B.C. (Erdosy 1989, 1995, Parpola 1995, Sarianidi 1999, see figure 11). Such arguments are supported by archaeological evidence. That is, the similarity of mortuary practices between Bactria-Margianan Archaeological Complex (BMAC) sites and archaeological sites found in northern India and in Pakistan suggests that the Late Bronze Age Central Asian inhabitants of BMAC sites are likely the source population for the “invasion” into Iran and the

Indus Valley during the mid-2nd millennium B.C. that subsequently spread gradually into northern India (Pyankova 1994; Hiebert & Lamberg-Karlovsky 1994). Sariandi (1994) and

Kuzmina (1995) argue that artifact styles, glyphs, and features suggest an ultimate Anatolian origin. This is evident in the stylistic similarities of temples related to Central Asian Bactrian-

Margianan Archaeological Complex sites10. Further, Indo-Aryan migration was likely a gradual process of emigration, not so much an “invasion” backed with military might. Parpola (1995) suggests that the prehistoric Vakhsh culture groups in southern Tajikistan most likely represent members of such a population. Sarianidi (1999) argues that Anatolians migrated southeast into Iran, then north into Central Asia, then southeast into India due via southern

Iran, or by direct travel across the Hindu Kush Highlands of Northern Pakistan. As such,

Sarianidi (1999) is also an advocate of the Early Entrance Model (described below).

Bamshad and coworkers (2001) argue from mtDNA haplogroup analysis that significant genetic differences are evident between South Asian groups based on caste affiliation. Further, this data suggests that high-status caste Hindus share greater genetic similarities to

9 This is because the models may be correct for one period, but not for another. 10 This is certainly true of Djarkutan, but not necessarily for the Kuzali and Molali Periods that make up different levels at the same location. 103

Europeans, thus reviving the Aryan Invasion hypothesis and suggesting that the current distribution of Indo-Aryan and Dravidian languages and the spread of traditional Vedic culture represents an invasion via mass-migration into India between the 3rd and 1st millennia B.C., which subsequently led to the development of the caste system through a history of elite dominance (also see Renfrew 1989 and Tartaglia et al.1995).11

Figure 11: Aryan Invasion Model: Southern Route (Sarianidi and Renfrew) and Northern Route (Kuzmina, Erdosy, Parpola).

11 This is to say that the southern position of the Dravidian languages is the result of South Indian populations’ retraction in the face of an Aryan invasion.

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The Long Standing Continuity Model

Proponents of the long-standing continuity model argue that over the past 60,000 years, no significant movements of populations into the Indian subcontinent have taken place

(Kennedy et al. 1984, see figure 12). This suggests the most significant event in the population history of the subcontinent was the initial colonization of the region between 50-60,000 years ago by anatomically modern humans dispersing out of Africa. Subsequently, for millennia, the

Hindu Kush mountains and the Himalayas served to largely isolate the human population of the subcontinent. Hence, since the Pleistocene, the biological history of the Indian population is one of long-standing in situ development unaffected by significant gene flow through migration from outside the subcontinent. Kennedy and coworkers (1984) argued, based upon craniometric assessment of 15 archaeologically derived samples from South Asia that high levels of heterogeneity between these groups suggest little or no gene flow occurred due to migration12. That is, regional samples conform to a strict pattern of isolation-by-distance, most likely caused by genetic drift. Kennedy and coworkers (1984) assert that small population sizes and marital patterns that likely conformed to a pattern of isolation-by-distance, as village occupants could not travel far in search of partners, increased the effect of genetic isolation leading to the pattern of diversity found in modern Indian populations.

Evidence in support of the Long-Standing Continuity model has been provided by a few studies. Bhasin and coworkers (1985) conducted an odontometric study that included samples from living ethno-linguistic groups in India. These samples were taken from living Bengali

Brahmins, Punjabi Khatris, Jats, Ahirs, Uttar Pradesh Bramins, Dangis, Kunbis, and Varlis

(Maharashtra). The results yielded significant differences between ethno- linguistic groups that reflected geographic distribution. That is, overall differences in tooth size allocation between all the groups were minimal, but analysis of groups based on geographical distances showed significant differences between far northern and far western Indian samples (Bhasin et al.1985:

12 It should be noted that some of Kennedy and coworkers’ samples consisted of single skulls.

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Figure 12: The Long Standing Continuity Model

86). Based on anthropomorphic and anthroposcopic research, Majumdar (1998) asserted that

Indian sub-populations demonstrate distinct genetic patterns that have resulted in varied phenotypic characteristics that suggest reasonable isolation of these groups from one another over the past 60,000 years. Moreover, the pattern of isolation-by-distance identified by

Majumdar (1998) suggests that geographic propinquity is a better predictor of biological affinities between populations in South Asia than language or other socio-cultural criteria.

Majumbdar (1998) offers the fact that all Indian groups share Arab-Indian haplotypes, regardless of language, class, or geographic location as evidence against ancient invasions of the subcontinent by Central or West Asian invaders.

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The Early Entrance Model

Figure 13: The Early Entrance Model

Renfrew (1987) offers two hypotheses to explain the current distribution of archaeological and linguistic patterns across South Asia. The first is denoted as “hypothesis

A,” the Neolithic Arya Hypothesis, and the second is “hypothesis B,” which provides an alternative explanation (see Figures 13 and 14). To begin with hypothesis A, Renfrew (1987) argues that the arrival of populations that spoke Indo-European languages in South Asia likely occurred contemporaneously with the arrival of other Indo-European speaking farmers in

Europe whose ultimate origins may be traced to Anatolia. Evidence of farming at Mehrgarh, in

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west-central Pakistan, suggests that cereal crops were cultivated in the Indus Valley as early as

6,000 B.C. (Jarrige 1981, 1985, Jarrige et al.1979, Jarriage and Lechivallier 1980). Renfrew

(1989) argues it is likely that the prehistoric inhabitants of the Indus Valley spoke a variant of proto-Indo-European. Moreover, Renfrew (1985) notes that archaeological features such as the great bath at Mohenjo-daro likely represent early manifestations of Hindu ceremonial culture and that these alleged early manifestations of Vedic culture bear close resemblance to features found at archaeological sites in Central Iran. Interestingly, in spite of Wheeler’s (1968) claims that the skeletons found sprawled in the streets of Mohenjo-daro represent the victims of a violent wave of Aryan Invaders, more recent archaeological evidence suggests that, in spite of the obvious dissolution of the urban centers

Figure 14: Renfrew’s (1987) hypothesis A (continuous line) & hypothesis B (dotted line).

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of the Indus Valley Civilization, rural life and culture appears to have been unaffected by this alleged turmoil.13 Religious connections between Hindus in peninsular India and the prehistoric occupants of the Indus Valley are further suggested by the fact that Indus seals depict motifs that resemble Hindu dieties, depictions of linghams, and frequently display bovines, which are held sacred by practicioners of the Hindu faith. Allchin and Allchin (1982) provide archaeological evidence that suggests the prehistoric occupants of the Indus Valley may have been Indo-Iranian speakers. Allchin and Allchin (1982) state:

Such ‘ritual hearths’ are reported from the beginning of the Harappan period itself. It has been suggested that they may have been fire-altars, evidence of domestic, popular, and civic fire-cults or the Indo-Iranians, which are described in detail in the later Vedic literature. It may then be an indication of culture contact between an early group of Indo-Aryans and the population of the still flourishing Indus Valley civilization (191).

Overall, Renfrew’s (1987) Neolithic Arya hypothesis implies early connections between West-

Central Asian populations and the occupants of the Indus Valley.

Alternativly, but not exclusively, Renfrew’s (1987) hypothesis B assumes that Central

Asian pastoral nomads from the Russian steppes spread first across the Iranian Plateau and then across Chitral and the Hindu Kush Highlands into Northern India on horseback, bringing with them pack-animals and wheeled carts14. This hypothesis is the Aryan invasion model and places the entrance of these invaders circa 1,500 B.C. This hypothesis, unlike hypothesis B, suggests that Indo-European languages were spread from the Russian steppes to India and

Iran and maintained via elite dominance. These invaders allegedly made use of horses and chariots, which allowed them to maintain dominance over conquered non-Indo-European peoples. Renfrew (1987) notes that these hypotheses may not be exclusive, except for the timing of the split between the Iranian and Indic languages, which are purported to have occurred circa 1,500 B.C. He argues further that if Anatolia is assumed to be the homeland for

13 See also Doles (1964).

14 It should be noted that ox-driven wheeled carts are known from Harappan contexts. However, apart from two-wheeled horse-drawn chariots, there is no advantage to making such a conection (but see Anthony 2010).

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Indo-European languages, then a series of invasions may account for the current distribution of languages and the patterns of archaeological similarity found between Central and South

Asia. This is, of course, a big assumption. Nonetheless, assuming Anatolia is the Indo-

European homeland, the Renfrew’s hypothesis A holds that Indo-European speakers entered the northern periphery of the sub-continent, settled in the Indus Valley, and subsequently moved south into peninsular India. These early immigrants brought with them farming technology, which spread with them into southern India subsequent to their arrival in

Baluchistan circa 6,000 B.C. According to hypothesis B, a second invasion that likely introduced the Indo-Aryan languages to South Asia occurred around 1,500 B.C., after the divergence of the Indo-Iranian and Indic language families. This second migration was carried out by horse-mounted pastoralist raiders, who conquered the local populations and maintained leadership and linguistic superiority over them via a system of elite dominance, which later developed into the Hindu caste system. In conclusion, it is important to note that proponents of the Early Entrance Model are divided over the validity of Hypothesis B; that is, many do not adhere to notions of a 2nd Millenium B.C. Aryan Invasion of India.

The Historic Era Interactions Model

The Historic Era Interactions Model was recently introduced in a series of studies conducted by members of the Centre for South Asian Dental Research (see Hemphill 2008,

Blaylock 2008, O’Neill and Hemphill 2009, 2010, Hemphill and coworkers 2012, Guzman and Hemphill 2012, Hemphill 2012, O’Neill and Hemphill 2012, see

Figure 15). This model recognizes the possibility that significant population movements have occurred since the end of the Bronze Age. This model is especially relevant for Pakistani populations because of the variation in languages and cultural traditions among them. For instance, population movements of outsiders into and out of Pakistan are alleged to have occurred frequently between A.D. 1000 and A.D. 1900. Such population movements involved the development of Buddhism and its spread into Asia, Mughal conquests of northern India and frequent raids and contests of power between Afghans and the local populations of

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northern India and Pakistan (see for example Biddulf 1888, Stein 1929, Sharani 2004, Dani

2006). Moreover, the “Great Game,” featuring colonial competition between Russia and Great

Britain around the turn of the 20th century, resulted in the re-settlement of entire ethnic populations that claim indigenous occupancy of eastern Afghanistan, Uzbekistan, Tajikistan, western China, and northern Pakistan. Proponents of the Historic Impacts Model holds that many, but not all, of the ethnic groups that currently inhabit northern Pakistan are recent immigrants (Hemphill et al.2012).

Figure 15: The Historic Era Interactions Model

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Expectations

The general research questions that will guide this study are presented below:

1.) Does odontometric variation among living Hindu Kush Karakoram Highland populations demonstrate patterns of biological affinities between ethnic groups according to language, status (class, caste), and geographic proximity? Are historical, linguistic, and geographically-based ethnic classifications commonly used in demographic studies biologically reliable?

2.) Which of the models for the population history of South Asia are best supported by the patterning of biological affinities possessed by living and prehistoric ethnic groups considered in this study?

For the first question, the proposed answer is yes and no. That is, based on the results of previous tooth-size allocation analyses obtained by O’Neill and Hemphill (2010, 2011, 2012),

Guzman and Hemphill (2012), and Hemphill (2012), biological affinities among ethnic groups considered in this study are consistant with linguistic affinities between groups as well as geographic propinquity. The results of these studies will be further supported if the geographically and temporally distinct Wakhi and Shina samples exhibit closest affinities to one another. This makes this study important because it has the ability to confirm the repeatability of these earlier studies and show that regardless of the context of compararive samples included, tooth-size allocation analysis can effectively identify close affinities between ethnic groups that share a common origin, but have subsequently become separated by harsh terrain and geo-political boundaries. Furthermore, to demonstrate the reliability of linguistic association in identifying population affinities, distinct, exclusive aggregates should be formed in each analysis that identify closest affinities between living groups that speak related languages (Dravidian, Indo-Aryan, Indo-Iranian).

As for the second question, the four models of South Asian population history considered in this study have specific hypotheses and expectations, as outlined below. Each has different implications concerning the origins of the Shina and Wakhi of northern Pakistan.

1.) The Aryan Invasion Model :

Support for the Aryan Invasion Model is provided if highland Pakistani ethnic groups, specifically the Wakhi or the Shin, are the living decendants of Central Asian invaders who 112

brought an Indo-Aryan language or languages to South Asia during the first half of the 2nd millennium B.C. Linguistic association suggests that the Shin, who speak a Dardic language, may possess such affinities, but the Wakhi, who speak an Indo-Iranian language may not (see

O’Neill and Hemphill 2010). In general, Pakistani highland groups that speak Indo-Aryan languages (the Shina and the Kho) should possess closest affinities to prehistoric Central

Asians, who are their alleged anscestors, followed by secondary affinities to Indo-Aryan speaking groups occupying northern India, such as the Bhils (BHI), Garasia (GRS) and Rajputs

(RAJ) of Gujarat. Moreover, one ought to find evidence of a post 1,500 B.C. discontiuity in phenetic affinities possessed by prehistoric Indus Valley samples demonstrating population affinities between samples from the Bactrian-Margianan Archaeological complex (SAP, DJR,

KUZ, MOL) and later Indus Valley samples from Timargarha (TMG) and Sarai Khola (SKH).

Clearly, both conditions are required to support this model in its strictest sence.

2. The Long-Standing Continuity Model :

The results will support the Long-Standing Continuity Model if phenetic affinities among samples conform to a pattern of isolation-by-distance. Hence, in order to support this model with odontometric analysis, the Wakhi and Shina must exhibit closest affinities to the other samples that are closest to them in time and space. Hence, there should be no close phenetic affinities between prehistoric populations from Central Asia and the Indus Valley.

Moreover, all living samples should form aggregates based on geographic propinquity.

Therefore, the Wakhi and Shina should exhibit closest affinities to one another, as well as to other ethnic groups of Pakistani highlanders, and all of these groups should exhibit secondary affinities to the prehistoric occupants of the Indus Valley, who are their alleged ancestors. If all requirements are met for this model to be supported, it will suggest that the Wakhi and Shin, along with many other highland Pakistani groups represent long-term indigenous residents of

Northern Pakistan.

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3. The Early Entrance Model

The Early Entrance Model suggests that the ancestors of the Wakhi, Shin, and other northern Pakistani highlanders, experienced gene flow some five to seven millennia ago due to a migration of Proto-Elamitic populations whose ultimate homeland may be traced to southwestern Iran. Hence, the Wakhi and Shina should possess distant but equal affinities to prehistoric Central Asians, living inhabitants of northwest India, and the latest samples from the Indus valley (TMG, SKH). Moreover, Dravidian-speaking South Indians should exhibit affinities to Chalcolithic era sample from Mehrgarh (ChlMRG) and to mature phase inhabitants of Harappa (HAR) located in the Indus Valley. As such there should be two breaks in Indus

Valley biological continuity for this model to be supported. That is, a break must occur between the Neolithic and Chalcolithic occupations of Mehrgarh (to allow for the introduction of refined farming techniques, mortuary practices, and ceramic technologies from outside of the subcontinent), and a second break between the Late Chalcolithic occupants of Harappa and the later prehistoric and historic Indus Valley samples.

4.) The Historic Era Interactions Model.

Proponents of the Historic Era Interactions Model suggest that many of the ethnic groups occupying northern Pakistan are recent immigrants. This model will be supported if samples from northern Pakistan consistently exhibit varied affinities. Living Pakistani groups, such as the Wakhi and Shin, should exhibit no affinities to prehistoric samples from the Indus

Valley (NeoMRG, ChlMRG, HAR, TMG, SKH) or to either prehistoric (INM) or living samples from peninsular India (BHI, CHU, GPD, GRS, PNT, RAJ). These highland groups should exhibit varied affinities to one another as well as to other samples from regions adjacent to

South Asia, such as southern Turkmenistan (ALT, GKS) or southern Uzbekistan (SAP, DJR,

KUZ, MOL) (O’Neill and Hemphill 2012).

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Chapter Seven: Previous Odontometric Studies Conducted by Members of the Centre for South Asian Dental Research

This work follows a series of studies focused on the dental anthropology of Central and

South Asia, the most recent of which have been primarily concerned with living ethnolinguistic populations in Pakistan. Interestingly, but not surprisingly, biodistance analyses based on odontometric measurements have produced consistent results in the identification of genetic relatedness among living South Asians based on linguistic and regionally based classification of samples. This fact deepens the current analysis and demonstrates the utility of odontometric and other dental anthropological approaches used to explain the population history of this region chronologically.

Hemphill and co-workers (2007, 2013) sought to identify patterns of phenetic affinities among highland Pakistani ethno-linguistic groups from an experiment of dental morphometrics. This study introduced dental samples collected from the living Kho, an Indo-

Aryan speaking ethnic group located in the Chitral District of northern Pakistan. Odontometric data collected from the Kho was compared with odontometric data collected previously from 12 archaeologically-derived populations from Central Asia and the Indus Valley, and seven living populations of India. The purpose of these studies was two-fold. First, Hemphill and coworkers (2013) sought to identify the likely origins of the Kho, who are said to be the ancestors of Central Asian immigrants who immigrated to Pakistan during the 13th century A.D. Second,

Hemphill and co-workers (2013) sought to delineate the relationship between dental morphological and metric traits. Only the odontometric results are considered here, but this study demonstrated that morphological and metric approaches produce broadly concordant results.

The preliminary odontometric results of Hemphill and co-workers’ (2013) study produced interesting patterns of phenetic affinities between sample groups across space and time. These results were used to test the four most prominent competing models for Indian population history described above. Cluster analysis and principal components analysis of the

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odontometric data reveals that the most significant division of samples occurs between prehistoric Central Asians and all peninsular Indian groups (Figures 16 and 17). A secondary separation among living Indians occurs according to linguistic affiliation, except for the mixed caste sample from Maharashtra, which likely reflects elements of both linguistic categories.

Lack of affinities between living peninsular Indian samples and the earliest samples from the

Indus Valley runs counter to the expectations of the Early Entrance Model. Since no affinities are identified between prehistoric Central Asians and any group other than the Kho, the Aryan invasion model is not supported by this data. Instead, these data are in greatest accord with a pattern of isolation by distance, both in time and in geographic space.

Interestingly, there appears to be a distant relationship between prehistoric Indus

Valley samples and all peninsular Indian samples, providing some support for the Long-

Standing Continuity model. The fact that the Kho share closest affinities to prehistoric Central

Asian samples from Djarkutan might be interpreted as support for the Aryan Invasion Model.

However, the lack of affinities between prehistoric Central Asian and all other samples suggest this is not the case. As such, it is much more parsimonious to interpret the affinities between the Kho and prehistoric Central Asians as a product of more recent immigration of Central

Asians into northern Pakistan than documented historically and portrayed in Khowari oral traditions. The lack of a chronological break in phenetic continuity between prehistoric Indus

Valley samples suggests that, in spite of cultural changes indicated by linguistic and archaeological data, the biological history of the subcontinent has been little affected by outsiders until well after the onset of the Iron Age (see also: Blaylock 2007).

Hemphill (2009b) conducted further research, this time focusing on the inhabitants of

Madak Lasht, an isolated village located in the remote and rugged mountains of Chitral. This group, unlike the Kho, does not speak a language that has been classified within the Dardic branch of Indo-Aryan languages, but instead speak an archaic form of an Indo-Iranian language known as Dari. Once again, Hemphill (2009) employed a morphometric approach and demonstrated that metric and non-metric traits produce highly concordant results in bio-

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distance analyses involving prehistoric and living samples in Central and South Asia. The results of this study produced the slightly different patterning of affinities, depicted in Figure

17, 18 and 19. (see also Hemphill 2009a).

Figure 16: Preliminary results of Cluster Analysis (Hemphill et al. 2012) presented at the 2007 AAPA meetings (Hemphill et al. 2007).

Three statistical analyses were employed in this study, producing somewhat different, yet generally consistent patterns of affinity based upon allocation of permanent tooth size. In fact, these results are very similar to those obtained in the earlier study by Hemphill and co-workers

(2007), except for the odd placement of the inhabitants of Madak Lasht. That is, the most significant division of samples occurs between prehistoric Central Asians and all other samples, except for the Kho. Once again, Indian samples are segregated by linguistic affiliation and geographic location, and there is a general pattern of isolation-by-distance across samples.

This time, however, the statistical methods produced conflicting results concerning the phenetic classification of the inhabitants of Madak Lasht. That is, the sample from this region

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falls into the aggregate containing prehistoric Indus Valley samples. At first glance, this might be interpreted as support for the Early Entrance Model. Furthermore,

Figure 17: Preliminary results of Principal Coordinates Analysis (Hemphill et al. 2012) presented at the 2007 AAPA meetings (Hemphill et al. 2007).

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Figure 18: Results of Cluster Analysis (Hemphill 2009).

Figure 19: Results of Neighbor-Joining Cluster Analysis (Hemphill 2009).

Figure 20: Results of Multidimensional Scaling (Hemphill 2009).

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the inhabitants of Madak Lasht do not fall into any aggregates in the subsequent two statistical analyses, suggesting a pattern of isolation-by-distance and recent displacement, rather than gene flow. As such, the Long Standing Continuity Model is supported by the patterning of prehistoric Central Asian and living Indian samples, while the placement of highland Pakistani samples suggests post Iron-age immigration of these groups into Pakistan, but fails to identify the source location of these population movements. Such results lend support the Historic Era

Interactions model, which cannot be tested more specifically without Iron Age, Medieval, and

Historic Era samples from Central and South Asia.

O’Neill and Hemphill (2009) conducted an ondontometric analysis focused on the origins of the living Wakhi, in this case samples drawn from the village of Gulmit, located in

Gilgit-Baltistan, Pakistan. The Wakhi are known for their traditional practice of highland agriculture and extreme high altitude pastoralism. The Wakhi, like the inhabitants of Madak

Lasht, speak an Indo-Iranian language. The Wakhi language, belonging to the Pamir language family, contains Old Persian archaisms that imply ancient movement of populations from the west and subsequent isolation of these travelers from other Indo-Iranian speaking groups (Dani

2007). The results of this study supported historical sources that document the immigration of

Wakhi settlers from the Wakhan Valley of Afganistan into the Gilgit-Baltistan in northern

Pakistan, where they likely founded the hamlet of Gulmit in the Gojal District between 1881 and 1907 (Felmin 1996, Sharani 1979, Buddulf 1888). The results of this study are depicted below (Figures 20, 21, 22). This study is the first of this series of odontometric investigations to identify a regional aggregate of Pakistani highland samples. Once again, the most significant difference between samples based on scaled geometric means occurs between the prehistoric

Central Asian samples and samples gathered from the prehistoric inhabitants of the Indus

Valley. Reasonable geographically defined aggregates are identified, regardless of statistical approach. Holding true to previous patterns of phenetic affinity that demonstrate parallelism with linguistic affiliations between groups, the living Indian samples are once again divided into neatly exclusive categories of Dravidian-speakers and Indo-Aryan-speakers, respectively. This

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is true for all living Indian samples, except for the mixed caste sample from Maharashtra and the highly divergent Chenchu sample. The extreme statistical isolation of Chenchus implies that Indian caste affiliation has also been a factor in the development of populations on the sub-continent that stands separated from linguistic association, which is sometimes a product of diffusion or in this case, the “Hinduization” of Indian tribal populations. Finally, these data best support the Long Standing Continuity Model, as none of the regional

Figure 21: Results of Cluster Analysis (O’Neill and Hemphill 2009).

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Figure 22: Results of Neighbor-Joining Cluster Analysis (O’Neill and Hemphill 2009).

Figure 23: Results of Multidimensional Scaling (O’Neill and Hemphill 2009).

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aggregates appear to share close affinities. That Pakistani populations share distant affinities with ancient Central Asians may support hypotheses of population movement, or more likely that northern Pakistani highland groups located at the periphery of South Asia may represent recent immigrants to these marginal areas during historic times. In either case, it appears that the Wakhi of Gulmit are most likely recent immigrants to Pakistan with linguistic affiliations linking their origins to the north and west, along with their highland neighbors, the Madak

Lasht, and the Kho. The Historic Era Interactions model is only partly supported by the fact that none of the living Pakistani highland groups share close affinities to one another, or any of the living or prehistoric samples south and west of Pakistan. The one exception to this is the distant phenetic ties between Altyn Depe in Turkmenistan (4,500 B.C.) and the living Wakhi.

This may imply Turkish origins for the earliest Wakhi settlers of the Wakhan Valley and surrounding regions, for linguistic evidence suggests this is true (McMahon and McMahon

2007). Unfortunately, samples from Iron Age, Medieval, and living Central Asians are necessary to trace such a movement with specificity and precision.

Willis (2010) participated in data collection in northern Pakistan and prepared a

Master’s thesis focused on tooth size allocation among the Burusho, another highland

Pakistani group, but one who speaks a linguistic isolate. Willis (2010) worked under Lorimer’s

(1935) assertion that the Brushaski language stands as a remnant language of a once larger indigenous population that was conquered by outsiders during the medieval era of Indian history (also see Biddulf 1888). In line with previous studies of this series, Willis (2010) sought to test the most prominent competing models for the population history of India, and also to find out whether

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Figure 24: Results of Heirarchical Cluster Analysis (Willis 2010). the Burusho represent an indigenous population of Northern Pakistan. The statistical analysis produced results congruent with previous studies (see Figures 23, 24, 25). That is, the most significant division of samples occurs between prehistoric Central Asians and all other groups, linguistic and caste associations are paralleled by the formation Dravidian-speaking and Indo-Aryan-speaking aggregates, and the living Chenchu tribals are not associated with

South Indian caste groups irrespective of their shared Dravidian linguistic affiliation. The

Chenchu stand apart from all other populations, and form a dyad with the Burusho. Overall, the phenetic patterning of samples used in this study support the Long Standing Continuity

Model and

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Figure 25: Results of Neighbor-Joining Cluster Analysis (Willis 2010).

Figure 26: Results of Multidimensional Scaling (Willis 2010).

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perhaps imply, as Willis (2010) suggests, that the Burusho represent an indigenous population of northern Pakistan. The problem with this logic is that the Burusho do not demonstrate any affinities with prehistoric Indus Valley samples, which are the only available samples upon which claims of indigenous heritage can be made with any certainty. This, combined with historical testimonies that suggest the Burusho more likely represent one of the earliest waves of non-native immigrants to the area, run contrary to Willis’ interpretation (Biddulf 1888).

Furthermore, the fact that none of the regional sample aggregates demonstrate significant affinities to one another lends support to the Historic Era Interactions Model, and suggests that use of historic and medieval samples will be necessary for a more definitive analysis of the biological history of South Asia during the Post Bronze Age eras.

In 2010, Hemphill introduced a new sample from the foothills of the Karakoram

Highlands, the Swati. The Swati consider themselves “Swati Pathans,” but their Pathan neighbors deny any implied shared ancestry. In support of this claim, Schofield (2003) argues that the Swati are descendants of Pashtun-speaking populations driven from the Swat Valley into the Hazara hills during between A.D. 1500 and 1700. Ibbetson (1916) argued that the

Swati have roots in peninsular India based on cultural characteristics that imply a Hindu background. Hemphill (2010) sought to test these historical assertions with morphometric data. This was done to identify the most likely origins of the living Swati of Hazara and to test the current four models of South Asian population history that have been the focus of this series of dental anthropological studies.

Hemphill (2010) reported interesting results. First, odontometric data and morphological data produced near equivalent results. Having said that, all statistical analyses isolated the aggregate containing prehistoric Central Asian samples from all archaeological and living populations of South Asia represented in the study. The Kho, once again are identified as sharing affinities with the prehistoric Central Asian sample from Djarkutan, but the morphological results contest this phenomenon. Overall, Hemphill’s data identified regional aggregates and demonstrated reasonable continuity of affinities based on proximity. Hemphill

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(2010) rejects Ibbeston’s (1916) hypothesis of peninsular Indian origins based on lack of affinities between the Swati and any of the South Asian samples.

Figure 27: Results of Hierarchical Cluster Analysis (Hemphill 2010).

Figure 28: Results of Neighbor-Joining Cluster Analysis (Hemphill 2010).

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Figure 29: Results of Multidimensional Scaling (Hemphill 2010).

Overall, the results of Hemphill’s (2010) study fail to support the Aryan Invasion Model, instead providing limited support for localized regional continuity among both archaeological and living populations. While these data demonstrate a reasonable pattern of isolation-by- distance both chronologically and geographically, Hemphill (2010) argues that lack of affinities between the Chenchu and other South Indian groups suggests a break in continuity among

South Indian populations at some time in the past. The Long Standing Continuity Model is also weakened by the lack of affinities between all living Pakistani highland samples. Further, morphological data demonstrates a break in continuity between the latest prehistoric Indus

Valley samples, Neolithic and Chalcolithic Mergarh. This break in continuity between prehistoric Indus Valley samples might be interpreted as evidence in support of the Early

Entrance Model, but the general lack of affinities between any of the living samples and the archaeological samples dismisses such evidence. The patterning of affinities among the samples considered in Hemphill’s (2010) study best support the Historic Era Interactions

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Model. This is evident in the fact that the latest samples from the Indus Valley, Sarai Khola and

Figure 30: Results of Principal Coordinates Analysis (Hemphill 2010).

Timargarh, share affinities with prehistoric Central Asian samples. Morphological results (see

Hemphill 2010) suggest a connection between living South Indian caste groups and post-

Neolithic samples from the Indus valley, collected from the Chalcolithic layers at Mehrgarh and mature phase levels at Harrapa. Lack of a conenection between living South Indians and more recent Indus Valley samples suggests that post Bronze Age population movements have occurred into and perhaps out of South Asia in the past. In this case, the patterning of affinities between Pakistani highland groups demonstrates isolatory divergence even at the local level. This implies support for the Historic Era Interactions Model. Hemphill (2010) argues that distant affinities identified between living Pakistani Highlanders and the inhabitants of Chalcolithic Mehrgarh offers support for Jarrige (1985) and Schofield’s (2003) studies that link northern Pakistan with southern Afghanistan based on archaeological similarities between contemporary sites in these regions. Hemphill (2010) argues that “such

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results are suggestive of relatively recent immigration [of Central Asian populations] into South

Asia.”

O’Neill and Hemphill (2010) conducted further odontometric research and introduced another sample collected from the living Wakhi residing in the extreme northern border village of Sost, Pakistan. The purpose of this study was two-fold. First, this study sought to test the models of Indian population history previously considered in all dental research conducted out of the Centre for South Asian Studies. Second, O’Neill and Hemphill (2010) sought to test the reliability of classifying ethnic groups based on linguistic affiliation, geographic location, and historical documentation. This was achieved by comparing two ethnic groups that likely share a common origin, but have since become separated by harsh terrain and socio-political boundaries. The results of this study are significant, if not ground-breaking.

Hierarchical cluster analysis identifies the two Wakhi samples as most similar to one another, with secondary affinities to other groups of the Hindu Kush highlands (see Figure 31).

This aggregate formed of living Hindu Kush highlanders possesses only distant affinities to living peninsular Indians and to prehistoric Central Asians, and extremely remote affinities to the prehistoric samples from the Indus Valley, the earliest prehistoric sample from Central Asia

(GKS), and the single prehistoric sample from western India (INM). Among these latter samples, the latest of the prehistoric Indus Valley samples (SKH) clusters with the prehistoric Central

Asians, while peninsular Indians segregate into two aggregates of Dravidian-speakers on the one hand and Indo-Aryan-speakers of west India on the other. The second earliest sample from

Central Asia (ALT) stands as an outlier to this aggregate of peninsular Indian samples.

Neighbor- joining cluster analysis shows some similarities and some differences in the patterning of sample affinities to those identified by hierarchical cluster analysis (see figure 32).

Once again, the two Wakhi samples exhibit closest affinities to one another and secondary affinities to other ethnic groups of the Hindu Kush highlands. However, in a marked departure from hierarchical cluster analysis, one of the BMAC samples from Central Asia, DJR, is

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identified as possessing close affinities to these groups of the Hindu Kush. The sample from

Altyn depe and Dravidian-

Figure 31: Results of Hierarchical Cluster Analysis (O’Neill and Hemphill 2010). speaking Chenchu tribals are identified as phenetic isolates with no affinities to any of the other samples included in this study. Remaining samples fall into three aggregates that may be identified as living peninsular Indians in the lower center, prehistoric BMAC samples in the lower right, and Indus Valley samples in the upper right. The two latest prehistoric Indus

Valley samples (SKH, TMG) occupy the most proximate positions to the BMAC samples from

Central Asia, while the earliest Central Asian sample (GKS) and the only prehistoric sample from west-central India (INM) are once again identified as possessing affinities to prehistoric inhabitants of the Indus Valley.

Multidimensional scaling of pairwise Euclidian distances into three dimensions with

Guttman’s method captures 99% of the observed variance in tooth size allocation between groups (see Figure 33). Once again, the two Wakhi samples, located in the center of this array, are identified as possessing closest affinities to one another, with secondary affinities to all other Hindu Kush highland samples, except one, the inhabitants of Madak Lasht. All 131

prehistoric Central Asian samples, except Altyn depe, which occupies an isolated position in the lower left of this array,

Figure 32: Results of Neighbor-Joining Cluster Analysis (O’Neill and Hemphill 2010).

Figure 33: Results of Multidimensional Scaling (O’Neill and Hemphill 2010). 132

exhibit closest affinities to one another and are identified as possessing distant affinities to the two latest prehistoric samples from the Indus Valley (SKH, TMG) on the one hand, and to the

Khowar of the Hindu Kush Highlands on the other. Peninsular Indians, including the prehistoric sample from west-central India (INM) occupy the upper center, but unlike the previous analyses there is no clear segregation between Dravidian- and Indo-Aryan-speakers.

Prehistoric samples from the Indus Valley show no internal continuity and are marked by wildly diverging affinities to Central Asians, to Hindu Kush highlanders, and even to peninsular

Indians.

The results obtained from three different statistical procedures consistently indicate that the two Wakhi groups, regardless of their different migration histories, are marked by nearly identical patterns of tooth size allocation throughout the permanent dentition. This finding demonstrates that, for the Wakhi at least, such ethnic identities, based on language, geography, and historical records, has biological meaning for anthropological investigations of the population history of Central and South Asia.

These analyses also consistently identify the Wakhi as possessing closest affinities to their fellow occupants of the Hindu Kush and Karakoram highlands. As a group these highlanders are marked by only remote affinities to prehistoric inhabitants of Central Asia, prehistoric inhabitants of the Indus Valley or to prehistoric and living inhabitants of peninsular

India. Pakistani Highlanders’ affinities to one another, coupled to their isolation from all other samples, are most consistent with the expectations of the Historic Era Interactions Model.

Patterns of affinity among remaining samples often identify living peninsular Indians as possessing closest affinities to one another and, in two analyses of three, segregated into

Dravidian-speakers on the one hand and Indo-Aryan-speakers on the other. Such results are most consistent with the Early Intrusion Model. However, running contrary to the expectations of this model is the failure to identify any affinities between these Dravidian-speakers of

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peninsular India to the ancient samples that immediately postdate this alleged incursion of proto-Elamo-Davidian-speakers (ChlMRG, HAR). All three analyses yield evidence of heterogeneity among the prehistoric inhabitants of the Indus Valley over time. The two earliest samples (NeoMRG, ChlMRG) are marked by closest affinities to one another and, in two of three analyses, to the prehistoric sample from west India. By contrast, two of three analyses identify the two latest prehistoric samples from the Indus Valley (TMG, SKH) as possessing closest affinities to prehistoric Central Asians. This latter result is most consistent with the Aryan

Invasion Model.

In conclusion, this multivariate analysis of the patterning of permanent tooth size allocation among 21 samples of prehistoric and living individuals of the Hindu Kush highlands, the Indus Valley, peninsular India and Central Asia has demonstrated the biological utility of using ethnic identities based on language, geography, and historical records for anthropological investigations of the population history of Central and South Asia. These analyses provide strong support for the Historic Era Interactions Model of recent intrusion of Hindu Kush highland populations into the northwestern periphery of South Asia. There is no support for the Local Continuity Model, except perhaps among living peninsular Indians. Instead there is some mixed support for both Early Intrusion and Aryan Invasion Models. However, the failure to identify affinities between living Dravidian-speakers and their alleged forebears in the Indus

Valley, as well as a failure to identify any affinities between prehistoric Central Asians and

Indo-Aryan-speaking west Indians, indicates that the models need further refinement and further testing before the population histories of Central and South Asia are better understood

(also see Hemphill 2011 and Barton and Hemphill 2011, O’Neill and Hemphill 2011, Hemphill and co-workers 2012, Hemphill 2012, and Guzman and Hemphill 2012).

Chapter Eight:

Results of Current Study

Heirarchical cluster analysis identifies, as found in previous studies, closest affinities between the two Wakhi samples and secondary affinities between all Wakhi and Shina samples

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(see Figure 34). Distant affinities are identified between these four samples from Gilgit-

Baltistan and the sample collected from the living Kho and from the archaeological sample collected from the Djarkutan period occupation of Djarkutan. The most significant difference between samples separates Central Asian samples from South Asian samples, with the exception of the living Wakhi, Shina, and Kho of northern Pakistan, who appear to have stronger biological affinities to Central Asians than to other populations of Pakistan and India.

All other samples demonstrate patterns consistent with geographic and/or temporal propinquity. Overall, three reasonable tempero-regional aggregates are identified by hierarchical cluster analysis. These consist of prehistoric Central Asians, living peninsular

Indians, and the prehistoric inhabtants of the Indus Valley. Living Indian caste samples (PNT and GPD, RAJ and GRS) form sub-clades that relate to language and geographic distance. The exceptions to this pattern are Chenchu tribals, who do not share significant affinities with any of the other groups considered, and Bhil tribals who share distant affinities to the low-status caste sample of Garasias from Gujarat, west India. Continuity is demonstrated among the prehistoric Indus Valley samples, but the latest (TMG, SKH) possess distant affinities to living peninsular Indians. That is, the prehistoric occupants of

Timargarha and Sarai Khola demonstrate a pattern of odontometric variation that is more similar to that of living peninsular Indians than to any other samples considered in this study.

Neighbor-joining cluster analysis produces results that are both similar and different from those obtained by hierarchical cluster analysis (Figure 35). The results of the two analyses are similar in that all of the Wakhi and Shina samples form an aggregate. This time, however, affinities between these groups and prehistoric Central Asian samples are only demonstrated through a distant connection with the prehistoric sample from Altyn Depe (ALT).

Three other regional aggregates are formed by this array that can be described as living

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Figure 34: Results of Hierarchical Cluster Analysis with Complete Linkage.

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Figure 35: Results of Neighbor-Joining Cluster Analysis. peninsular Indians in the bottom left, prehistoric Central Asians on the bottom right, and the prehistoric occupants of the Indus Valley in the top right. Exceptions to this pattern are the

Kho, who share affinities with the prehistoric sample from Djarkutan, the living inhabitants of

Madak Lasht, who appear unrelated to any other group considered in this study, and the inhabitants of Swat Valley, who appear to have distant affinities with living peninsular Indians.

Chenchu tribals are identified as an outlier in this array.

Kruskal’s multidimensional scaling explains 96% of the observed variance among samples (Figure 36). Closest affinities are identified between the two Shina samples (SHIa,

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Figure 36: Results of Kruskal’s Multidimensional Scaling.

SHIo), while second closest affinities are identified between the two Wakhi samples (WAKg,

WAKs). This time, however, the Shina samples occupy an isolated space, while the Wakhi samples are pulled toward the space occupied by prehistoric Central Asians. Once again, the

Kho samples occupy a phenetic space proximal to the prehistoric Central Asian samples. The inhabitants of Madak Lasht and the Swati occupy central positions as relative outliers, peninsular Indians occupy the top left of the array, while prehistoric occupants of the Indus

Valley occupy the lower left. This time, peninsular Indian samples are segregated according to language, but without regard to region or to caste versus tribal status. A reasonable degree of continuity is reflected by the patterning of affinities among the prehistoric samples from the

Indus Valley (NeoMRG, ChlMRG, HAR), but the latest samples (TMG, SKH) occupy an isolated aggregate in the bottom of this array.

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Figure 37: Results of Principal Coordinates Analysis.

Principal coordinates analysis captures 88% of the variance among samples (Figure 37).

Once again the focal samples of Wakhi- and Shina-speakers are marked by closest affinities to each other and segregate on the basis of ethnolinguistic identity. The occupants on the right of this array are living peninsular Indian samples above and prehistoric Indus Valley samples below. The lower left is occupied by prehistoric Central Asian samples, while the upper left and upper middle of this array contains living Pakistani highlanders, except for the Kho sample, which exhibits affinities to the prehistoric sample from Djarkutan. Once again, there appears to be breaks in Indus Valley continuity, for the latest samples (TMG, SKH) occupy an isolated space away from all other Indus Valley samples. In fact, NeoMRG also appears to be an outlier, possessing only distant affinities to Harappans and no affinities to ChlMRG.

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Discussion

The primary goal of this research has been to determine if highland Pakistani populations share close biological affinities to one another, or if they represent phenetically distinct groups. This was undertaken to assess the reliability of linguistic, archaeological, and historically based classification of ethnic groups commonly employed in demographic studies.

The second goal of this study is to test the four most current South Asian population history models with tooth-size allocation analysis. It is important to consider phenetic affinities based on tooth size to develop a more complete understanding of biological relationships and general patterns of microevolution among living Pakistani populations. Additionally, understanding patterns of phenetic affinities between groups in these regions can help answer important questions posed by linguists, archaeologists, geneticists and historians. Since it has been well established that tooth size allocation is strongly heritable and thus under reasonable genetic control, this method of estimating population affinities via phenotypic traits is justified in that the obtained results are meaningful in terms of genetic-relatedness between sample groups considered in this study.

Do highland Pakistani populations share close biological affinities to one another, or do they represent phenetically distinct groups? The answer to this question can be based upon both previous work and the results of the current study. The Wakhi samples from Gulmit and

Sost consistently exhibit closer affinities to one another than to any other group considered in this study. This occurs in spite of accounts that describe separate migration histories and fluctuating geopolitical boundaries. The same is true of the Shin. In fact, despite independent traditions, non-mutually intelligible dialects, and a segregated sense of ethnic identity, Shin populations appear almost identical phenetically. In general, however, it appears that highland

Pakistani populations have mixed affinities to populations occupying adjacent regions. This is demonstrated by the random placement of Pakistani highlander samples, other than the Kho, who share affinities with Central Asians, and the Wakhi and Shina who share affinities with each other, but do not share affinities with other Pakistanis. Consistently, Pakistani samples, 140

other than these two from Gilgit-Baltistan, change positions in the array as new samples are added to subsequent studies.

Does odontometric variation among living Karakoram highland populations demonstrate patterns of biological affinities between ethnic groups that correspond to language, status (class, caste), geographic proximity, or sex? There are no clear distinctions of

Pakistani ethnic groups based on linguistic affiliation, because groups that speak Indo-Aryan or Indo-Iranian are neither segregated nor clustered according to these criteria. However, proximity is identified as a likely catalyst for the independent aggregation of highlanders occupying Gilgit-Baltistan (Wakhi and Shina) and those inhabiting the mountains and valleys of Chitral (MDK, SWT, KHO). Further, the affinities between the Wakhi and Shin are distant.

The Wakhi appear to possess affinities to the Kho, but the Shin do not. None of these groups appears to share any affinities with the Swatis or the inhabitants of Madak Laskt. This fact supports Hemphill’s (1991) assertion that geographic propinquity is a stronger indicator of phenetic relatedness between ethnic groups than social status, language, or cultural affiliation among living Indian castes and tribal populations.

Interestingly, patterns of sex dimormphism appear to differ between the Wakhis and

Shin, the latter of which exhibits the classic “blunting” expected of populations exposed to systemic and prolonged undernutrition. Confirmation of this possibility should be considered a goal of future work with these samples. Turning to the accuracy of historical accounts in defining ethnic identities in highland Pakistan, it goes both ways. That is, it appears that as

Biddulf (1888) asserted different groups of Shina-speakers likely share a common origin.

However, the directionality of the Shina, or “Shin” place of origin does not conform with

Biddulf’s (1888) claim that they are descendants of Rajputs of the Kshyatria caste that once invaded the Indus Valley and pushed out groups of Burushos and Yashkuns. While the Wakhi are confirmed as recent immigrants, the origin of the Shin remains difficult to define. That is, lack of affinities between the Shin and any other living groups, except the Wakhi, who are closest to them in geographic proximity, along with lack of affinities to any archaeological

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samples, either from the Indus Valley to the south Central Asians to the northwest, suggest they may be indigenous to the Shinaki and Astore Valleys. Researchers are advised to consider this possibility in future work using these samples.

Having addressed populations on the local scale, it is now important to discuss the affinities of the Wakhi and the Shin from a wider perspective based on the population comparisons through multivariate analysis of tooth size allocation. That is, which of the models for the population history of South Asia are best supported by the patterning of biological affinities possessed by the living and prehistoric ethnic groups considered in this study? Also, where do Pakistani highlanders fit into the Central and South Asian continental versus the local regional phenetic landscape of northern Pakistan?

The data generated by this study produce an interesting array of results that are congruent, yet also stand as evidence against claims of support for the four models of South

Asian population history currently in vogue. While Willis (2010) argued that the Burusho were indigenous to the Karakoram highlands, an assertion that may carry merit, he interpreted his results in support of the Long Standing Continuity Model, which holds claim that no significant movements of outside populations into India have occurred during the past 60,000 years

(Kennedy 1999, also see O’Neill and Hemphill 2008). This claim is negated by the results of the current study that identifies affinities between some of the highland Pakistani groups and prehistoric Central Asians (in particular the Kho) in all of the four statistical analyses. Despite the fact that three temporal and regional aggregates are formed in these statistical outputs

(peninsular Indians, prehistoric Central Asians, prehistoric occupants of the Indus Valley) the same cannot be claimed for living inhabitants of the Hindu Kush and Karakoram highlands.

This is especially true for the Madak Lasht (MDK), who appear to have either anomalous or no affinities to any of the other samples included in this analysis. As well as for the Khowar of

Chitral, who appear to have Central Asian origins. Indeed, while the two Wakhi samples exhibit closest affinties to one another, as do the two Shina samples, these samples only share distant affinities to the Khowar and no affinities whatsoever to the inhabitants of Madak Lasht.

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Hence the Long Standing Continuity Model cannot be supported, at least from a continental perspective, although it can be applied - to some degree -in the local regional & temporally significant perspective. Nevertheless, archaeologically-derived samples from the Indus Valley clearly yield evidence of a break in biological continuity in which post-Harappan samples differ from those that predate them, a phenomenon that has been interpreted in the past as evidence of a mid-2nd millenium Aryan Invasion (Erdosy 1996, Sairianidi 1999, Dani 2006).

Yet, the Aryan Invasion model has been criticized and disproven by numerous studies over the past 20 years. Nevertheless it continues to populate the pages of world history textbooks in the United States, European nations, and India. The model is old, infamous in its derogatory power, its colonial overtones, and is mentioned here only to further discount its merit by demonstrating that there is no biological evidence to support any Bronze Age invasion of South Asia from Central Asia during the 2nd millennium B.C. First it should be noted that there are no affinities between the Bronze Age Central Asian samples and any of the post-

Harappan samples from the Indus Valley (TMG, SKH). Second, there is no link between any of the peninsular Indian samples and Central Asia. To be sure, there does appear to be a link between living Indians and the prehistoric inhabitants of the Indus Valley, yet this is a very distant affinity. In fact, this distant affinitiy all but disappears between the living peninsular

Indian samples and the post Harrappan phase samples from the Indus Valley. Hence, there is no support for the Aryan Invasion Model, despite connections between the living Kho and prehistoric Central Asians, first demonstrated by Blaylock (2008).

By association, the Early Entrance Model must be modified. To review, this model proposes an early entrance of Central or West Asians into South Asia via an Elamitic route followed perhaps by another invasion of Aryan speakers during the 2nd millenium. This second invasion has already been thrown out. While support was offered for the first half of this model by Hemphill (2010), the results of the current study provides evidence that leads to rejection of this model altogether. Nonetheless, there appears to be discontinuities in Indus Valley long before the post-Harrapan phase. Additionally, there are no affinities between Central Asian

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samples and any of the Indus Valley or peninsular Indian samples. Hence, it appears that continuity of artifact and motif styles between sites in West-Central Asia and sites in the Indus

Valley is not a reliable indicator of biological affinities between related burial populations nor movement of such populations across space (Hemphill 1999). Instead, such patterns are more likely indicative of diffusion via trade.

Having provided evidence against the other three models, the Historic Impacts Model is the only model that is given support by this analysis of tooth size allocation. The phenetic patterning of samples indicates that Risley’s (1915) assertion that Pakistani populations are most likely a mix of indigenous groups and others that have recently immigrated from Central

Asia and from China is most likely true. This is because none of the samples collected from populations in Chitral are classified consistently within any aggregate, except for the Kho which clearly posses distant affinities with Bronze Age Central Asians. The fact that there are no shared affinities between prehistoric Central Asians and the Latest Indus Valley samples suggest that this Central Asian connection is the result of population movements that occurred after 200 B.C., as suggested by Risley (1915). The failure to identify consistent aggregates containing Pakistani highlanders suggests there have been significant population movements into this region over the last 1000 years, a condition that has greatly increased, according to many historical sources, over the past 300 years (see for example Dani 2006). On the other hand, consistent grouping of the samples of Shin and Wakhi from Gilgit-Baltistan suggest that populations in this region are potentially of indigenous origins, but it must be remembered that the boundaries between Central Asia and northern South Asia are blurred, especially from a biological perspective. Overall, it appears that Gilgit-Baltistanis and highland populations from

Chitral do not share common origins, nor is it likely that a “Dardic” ethnicity can be accurately applied to all northern Pakistani ethnic groups that speak Indo-Aryan languages. This may be a result of marriages between proximal groups such as the Wakhi and the Shina who must seek out mates outside of their villages in order to avoid breaking incest taboos. Finally, and perhaps most importantly, is the fact that neither small dialectical differences nor the

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occupation of different geographic locations between the two Shina groups considered in this study had any affect on their biological affinities. Therefore, it appears that ethnic classifications based on linguistic familiarity have biological meaning, and are appropriate and meaningful when used properly in demographic studies.

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Appendix A: Descriptive Data Wakhi (Gulmit) LI1MD LI1BL LI2MD LI2BL LCMD N of cases 134 122 139 129 141 Minimum 4.040 4.140 4.460 4.490 5.170 Maximum 7.250 7.950 7.520 7.330 7.430 Mean 4.981 5.747 5.480 5.998 6.300 Standard Dev 0.482 0.466 0.436 0.466 0.388 LCBL LP3MD LP3BL LP4MD LP4BL N of cases 139 140 139 133 134 Minimum 4.800 5.480 5.010 5.000 5.640 Maximum 8.500 7.510 8.760 7.940 9.280 Mean 6.885 6.406 7.390 6.454 7.960 Standard Dev 0.596 0.399 0.525 0.527 0.566 LM1MD LM1BL LM2MD LM2BL UI1MD N of cases 136 136 120 120 135 Minimum 8.950 8.430 8.100 8.010 5.090 Maximum 12.310 12.040 11.840 11.260 9.660 Mean 10.658 10.411 9.766 9.929 7.938 Standard Dev 0.622 0.602 0.695 0.558 0.621 UI1BL UI2MD UI2BL UCMD UCBL N of cases 134 133 130 136 136 Minimum 5.560 4.720 4.200 5.530 5.370 Maximum 9.460 8.080 8.070 8.420 9.260 Mean 6.997 6.114 6.063 7.106 7.522 Standard Dev 0.611 0.601 0.647 0.468 0.727 UP3MD UP3BL UP4MD UP4BL UM1MD N of cases 139 140 134 134 134 Minimum 5.150 5.820 4.630 7.340 6.270 Maximum 7.800 9.960 11.090 11.620 12.370 Mean 6.345 8.456 6.027 8.746 9.938 Standard Dev 0.465 0.597 0.926 0.679 0.734 UM1BL UM2MD UM2BL N of cases 135 121 125 Minimum 8.740 6.120 8.600 Maximum 12.620 12.720 12.270 Mean 10.813 9.420 10.530 Standard Dev 0.730 0.850 0.737

Wakhi (Sost) LI1MD LI1BL LI2MD LI2BL LCMD N of cases 147 149 159 157 155 Minimum 4.000 4.200 4.480 4.330 5.070 Maximum 141.000 143.000 153.000 151.000 149.000 Mean 6.799 7.551 7.260 7.800 8.051 Standard Dev 13.511 13.520 14.097 13.921 13.797 LCBL LP3MD LP3BL LP4MD LP4BL N of cases 153 157 156 145 144 Minimum 5.020 5.290 4.820 4.940 5.500 Maximum 147.000 151.000 150.000 139.000 138.000 Mean 8.663 8.162 9.075 8.244 9.695 Standard Dev 13.603 13.879 13.705 13.216 12.957 LM1MD LM1BL LM2MD LM2BL UI1MD N of cases 163 161 122 125 161 Minimum 6.470 7.120 8.310 7.550 6.340 Maximum 157.000 155.000 116.000 119.000 155.000 Mean 12.280 12.094 11.374 11.443 9.805 Standard Dev 13.682 13.592 11.431 11.601 13.879 UI1BL UI2MD UI2BL UCMD UCBL N of cases 158 154 154 145 141 Minimum 4.700 4.490 4.150 5.450 5.280 Maximum 152.000 148.000 148.000 139.000 135.000 Mean 8.763 7.837 7.856 8.876 9.347 Standard Dev 13.863 13.759 13.764 13.118 12.833

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UP3MD UP3BL UP4MD UP4BL UM1MD N of cases 153 156 150 152 160 Minimum 5.000 6.660 4.600 6.340 8.260 Maximum 147.000 150.000 144.000 146.000 154.000 Mean 8.088 10.163 7.804 10.451 11.724 Standard Dev 13.665 13.562 13.556 13.309 13.578

UM1BL UM2MD UM2BL N of cases 161 111 123 Minimum 8.870 6.890 8.710 Maximum 155.000 105.000 117.000 Mean 12.678 10.880 12.274 Standard Dev 13.517 10.755 11.350

Shin (Astore) LI1MD LI1BL LI2MD LI2BL LCMD N of cases 146 136 149 148 151 Minimum 4.000 4.200 4.300 4.700 5.200 Maximum 140.000 130.000 143.000 142.000 145.000 Mean 6.740 7.484 7.288 7.729 8.130 Standard Dev 13.531 12.852 13.615 13.511 13.624 LCBL LP3MD LP3BL LP4MD LP4BL N of cases 152 159 159 154 153 Minimum 5.100 5.300 6.000 4.900 6.400 Maximum 146.000 153.000 153.000 148.000 147.000 Mean 8.768 8.250 9.137 8.224 9.816 Standard Dev 13.597 14.025 13.914 13.745 13.472 LM1MD LM1BL LM2MD LM2BL UI1MD N of cases 158 157 147 147 155 Minimum 8.700 9.100 8.200 8.600 5.200 Maximum 152.000 151.000 141.000 141.000 149.000 Mean 12.487 12.288 11.536 11.739 9.822 Standard Dev 13.416 13.387 12.937 12.917 13.584 UI1BL UI2MD UI2BL UCMD UCBL N of cases 154 158 156 158 155 Minimum 5.500 4.200 4.800 5.600 6.000 Maximum 148.000 152.000 150.000 152.000 149.000 Mean 9.004 8.073 8.011 9.003 9.468 Standard Dev 13.622 13.987 13.892 13.869 13.649

UP3MD UP3BL UP4MD UP4BL UM1MD N of cases 162 162 162 162 161 Minimum 5.200 6.400 4.800 6.100 8.300 Maximum 156.000 156.000 156.000 156.000 155.000 Mean 8.155 10.299 7.783 10.421 11.833 Standard Dev 14.174 13.902 14.222 13.885 13.643 UM1BL UM2MD UM2BL N of cases 163 134 138 Minimum 9.200 8.200 8.500 Maximum 157.000 128.000 132.000 Mean 12.592 11.298 12.198 Standard Dev 13.662 12.166 12.274

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Shin (other) LI1MD LI1BL LI2MD LI2BL LCMD N of cases 90 88 100 100 100 Minimum 4.400 4.800 4.900 5.100 5.500 Maximum 7.200 7.300 6.800 7.400 7.700 Mean 5.157 5.905 5.717 6.145 6.447 Standard Dev 0.425 0.490 0.431 0.454 0.456 LCBL LP3MD LP3BL LP4MD LP4BL N of cases 100 101 101 98 98 Minimum 5.800 5.400 5.900 5.000 6.800 Maximum 9.200 7.900 8.800 12.000 10.900 Mean 7.051 6.606 7.380 6.616 7.967 Standard Dev 0.641 0.454 0.522 0.754 0.667 LM1MD LM1BL LM2MD LM2BL UI1MD N of cases 102 102 88 88 97 Minimum 6.600 6.900 8.600 8.800 4.700 Maximum 12.400 12.000 11.600 12.200 9.300 Mean 10.785 10.560 9.909 10.142 8.154 Standard Dev 0.787 0.735 0.640 0.672 0.744

UI1BL UI2MD UI2BL UCMD UCBL N of cases 92 97 92 101 101 Minimum 5.600 4.900 4.700 5.400 5.900 Maximum 9.600 7.700 8.300 8.700 9.700 Mean 7.378 6.463 6.354 7.357 7.625 Standard Dev 0.769 0.591 0.689 0.555 0.726

UP3MD UP3BL UP4MD UP4BL UM1MD N of cases 99 99 95 97 102 Minimum 5.600 5.800 5.100 5.700 9.100 Maximum 8.500 10.000 11.000 10.300 11.900 Mean 6.560 8.654 6.172 8.681 10.277 Standard Dev 0.449 0.632 0.673 0.662 0.682

UM1BL UM2MD UM2BL N of cases 101 81 81 Minimum 9.300 8.100 8.100 Maximum 13.000 11.700 13.100 Mean 10.907 9.565 10.479 Standard Dev 0.772 0.798 0.812

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Appendix B: Geometric Means by Sample

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Appendix C: Euclidean Distance Matrix

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