The 82nd Annual Meeting of the American Association of Physical Anthropologists (2013)

The correlation of skeletal and molecular data: Concordance of cranial, dental, mitochondrial DNA, and Y-Chromosome DNA


1Department of Anthropology, Texas State University-San Marcos, 2Department of Anatomy and Biological Anthropology, Saga Medical School, 3School of Natural Sciences, School of Social Sciences, Arts, and Humanities, University of California, Merced, 4Department of Anthropology, University of Tennessee, Knoxville

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Genetic and craniometric data have previously been shown to be in concordance with each other, suggesting that they both express the same microevolutionary forces (Relethford, 1994, Gonzalez-Jose et al., 2004). However, peopling of the New World studies often differ in opinion about using molecular data vs. osteological data, as they appear to demonstrate differing patterns. The purpose of this study is to utilize nonmetric and metric cranial data, dental data, mtDNA haplogroup frequencies, and y-chromosome allele frequencies to ascertain microevolutionary patterns related to peopling of the New World.

The craniometric and nonmetric data were collected by the second author (TH) and the molecular data was obtained through previously published research. To best approximate each other, the molecular and skeletal data were taken from similar populations and regions. These populations include Inuits from Alaska, Asia, Canada, and Greenland, along with a sample of Aleutians. Kinship, R matrices, and distance matrices were obtained. Mantel tests were performed to assess the correlation between data sets, as well as Procrustes analyses. The statistics demonstrate significant correlations, some being 90% or higher, between the R matrices and kinship matrices. Thus, when investigating populations with multiple lines of biological evidence, differences may be attributable to sampling error and disparities in population composition, rather than disagreement of data types. In relation to peopling of the New World, interpretations should focus on reconciling the evidence from different data types, rather than discounting their ability to model population affinities.

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