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We have newly constructed an ethnohistorical database consisting of 3460 records of ethnic locations and movements in Europe since 2200 B.C. Using this database, we computed vectors of proportions that peoples speaking various language families contributed to the gene pools of 22161° x 1° land-based quadrats of Europe. From these vectors we computed ethnohistorical distances as arc distances between all pairs of quadrats. We used these distances as predictors of genetic distances, which we calculated independently from 26 genetic systems. We find significant partial correlations between ethnohistorical and genetic distances when geographic distance, a common causative factor, is held constant. Ethnohistorical distances explain a significant amount of the genetic variation observed in modern populations. These results are highly robust to simulated errors in and omissions from the ethnohistorical database. Randomization tests show that the historical sequence of the movements does not affect estimates of the ethnohistory-genetics correlation, but the geographic locations of movements do. We track the development of the ethnohistory-genetics correlation through time and show it to be gradual and cumulative over the past 4200 years. [ABSTRACT FROM AUTHOR]

Presents information on a study which examined the cancer mortalities in Europe and how the incidences are related to geographic, ethnohistoric and genetic distances. Environmental factors known to affect cancer rates; Materials and methods of the study; Results and discussion.

Focuses on whether measures of ethnohistorical differences between populations in Europe, correlate with their differences in mortality rates due to various cancers. Comparison of the effects of ethnohistory with those of genetics; Reference to the usage of European databases; Methodology used in the analysis; Results of the analysis.

Computes the vectors of proportions that peoples speaking various language families contributed to the gene pools of 2216 land-based quadrants of Europe using an ethnohistorical database. Genetics-ethnohistory correlations; Role of chronology and of location of the actions; Randomization of location.

From 420 records of ethnic locations and movements since 2000 B. C., we computed vectors describing the proportions which peoples of the various European language families contributed to the gene pools within 85 land-based 5 × 5-degree quadrats in Europe. Using these language family vectors, we computed ethnohistorical affinities as arc distances between all pairs of the 85 quadrats. These affinities are significantly correlated with genetic distances based on 26 genetic systems, even when geographic distances, a common causative factor, are held constant. Thus, the ethnohistorical distances explain a significant amount of the genetic variation observed in modern populations. Randomizations of the records by chronology result in loss of significance for the observed partial correlation between genetics and ethnohistory, when geography is held constant. However, a randomization of records by location only results in reduced significance. Thus, while the historical sequence of the movements does not seem to matter in Europe, their geographic locations do. We discuss the implications of these findings. © 1993 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We introduce a statistical protocol for analyzing spatially varying data, including putative explanatory variables. The procedures comprise preliminary spatial autocorrelation analysis (from an earlier study), path analysis, clustering of the resulting set of path diagrams, ordination of these diagrams, and confirmatory tests against extrinsic information. To illustrate the application of these methods, we present incidence and mortality rates of 31 organ- and sex-specific cancers in Europe; these rates vary markedly with geography and type of cancer. Additionally, we investigated three factors (ethnohistory, genetics, and geography) putatively affecting these rates. The five variables were correlated separately for the 31 cancers over European reporting stations. We analyzed the correlations by path analysis, k-means clustering, and nonmetric multidimensional scaling; coefficients of the 31 path diagrams modeling the correlations vary substantially. To simplify interpretation, we grouped the diagrams into five clusters, for which we describe the differential effects of the three putative causes on incidence and mortality. When scaled, the path coefficients intergrade without marked gaps between clusters. Ethnic differences make for differences in cancer rates, even when the populations tested are ancient and complex mixtures. Path analysis usefully decomposes a structural model involving effects and putative causes, and estimates the magnitude of the model's components. Smooth intergradation of the path coefficients suggests the putative causes are the results of multiple forces. Despite this continuity of the path diagrams of the 31 cancers, clustering offers a useful segmentation of the continuum. Etiological and other extrinsic information on the cancers map significantly into the five clusters, demonstrating their epidemiological relevance., (Copyright 2003 Wiley-Liss, Inc.)