Comparability of multiple data types from the bering strait region: Cranial and dental metrics and nonmetrics, mtDNA, and Y‐chromosome DNA

Abstract

Different data types have previously been shown to have the same microevolutionary patterns in worldwide data sets. However, peopling of the New World studies have shown a difference in migration paths and timings using multiple types of data, spurring research to understand why this is the case. This study was designed to test the degree of similarity in evolutionary patterns by using cranial and dental metric and nonmetric data, along with Y-chromosome DNA and mtDNA. The populations used included Inuits from Alaska, Canada, Siberia, Greenland, and the Aleutian Islands. For comparability, the populations used for the cranial and molecular data were from similar geographic regions or had a shared population history. Distance, R and kinship matrices were generated for use in running Mantel tests, PROTEST analyses, and Procrustes analyses. A clear patterning was seen, with the craniometric data being most highly correlated to the mtDNA data and the cranial nonmetric data being most highly correlated with the Y-chromosome data, while the phenotypic data were also linked. This patterning is suggestive of a possible male or female inheritance, or the correlated data types are affected by the same or similar evolutionary forces. The results of this study indicate cranial traits have some degree of heritability. Moreover, combining data types leads to a richer knowledge of biological affinity. This understanding is important for bioarchaeological contexts, in particular, peopling of the New World studies where focusing on reconciling the results from comparing multiple data types is necessary to move forward.

Testing the association between human mid‐facial morphology and climate using autosomal, mitochondrial, Y chromosomal polymorphisms and cranial non‐metrics

“…The correlation between autosomal SNP and cranial non-metrics matrices is also fairly high in both cases but does not reach significance. This is in agreement with the results of some previous works showing a certain correspondence between cranial non-metric and genetic data (Ricaut et al, 2010: Herrera et al, 2014.…”

“…More generally, correlations between intergroup neutral genetic distances and those based on cranial morphology have been the focus of many studies (Roseman, 2004;Gonzalez-Jose et al, 2004;Relethford, 2004;Roseman and Weaver, 2007;Smith, 2009;von Cramon-Taubadel, 2009b;Relethford, 2010;Herrera et al, 2014). But there are several important issues which require further consideration and that are related to both genetic and morphometric data used in such studies.…”

“…Second, while correlations between genetic and morphological distances are moderate to high and significant at the global level (Gonzalez-Jose et al, 2004;Roseman, 2004;Roseman and Weaver, 2007;Smith, 2009;von Cramon-Taubadel, 2009a,b;Relethford, 2010) this is often not the case at the continental or regional levels (Hernandez et al, 1997;Wilder, 2004;Perez et al, 2009;Herrera et al, 2014). Correlations are also usually much lower when the mid-facial skeleton is considered rather than the whole skull (Harvati and Weaver, 2006;Smith, 2009;von Cramon-Taubadel, 2009a,b).…”

Do nuclear DNA and dental nonmetric data produce similar reconstructions of regional population history? An example from modern coastal Kenya

“…Two recent publications also examine the agreement between paired genetic and morphological data using archaeological samples. First, Ricaut et al (2010) examined mixed cranial and dental morphology samples to estimate kinship within a single site, while Herrera et al (2014) examined cranial metric and morphological variation to understand population history at a regional level. Both acknowledge the value of skeletal variants in bioarchaeological studies at these geographic scales.…”

“…Ricaut et al (2010) found that combined morphological data provided good resolution in identifying pairs of kin within a Mongolian necropolis, although genetic findings detected double the number. In the second study, Herrera et al (2014) compared Y-chromosome, mtDNA, cranial metric, and cranial morphology data in samples from the Bering Strait region; they found that craniometric distances were correlated with mtDNA, while distances based on cranial morphology were correlated with those from Ychromosome variants. Viewed as a whole, two lessons can be learned from the present and previous studies: 1) additional work needs to be undertaken to determine which skeletal and genetic data are best suited to answer particular research questions (Herrera et al, 2014;present study); and 2) care should be taken when formulating very fine scale interpretations of population history from skeletal data (Ricaut et al, 2010;present study;also Scott and Turner, 1997).…”