Classifications of European populations were produced based on 59 gene frequencies and 10 cranial measurements, but recorded for different population samples. A map-quadrat approach circumvented the problem of noncorrespondence of sampling localities. Clustering by standard numerical taxonomic procedures shows that these data are represented poorly as hierarchic classifications, but are associated with geographic regions. The agreement between classifications based on gene frequencies and those based on cranial variables is substantial (88%). Ordinations show genetic and morphometric continuity among these populations, although there is good regional separation on the whole. The relationships between phenetics, geography and language affiliation were examined by quadratic assignment methods. Both gene frequency and craniometric distances show significant correlations with geographic distances. The expected significant correlation between geographic distances and language affiliation is demonstrated. The gene frequency distances also show significant correlations with language distances. None of the partial correlations are significant. These analyses demonstrate a significant association of phenetics with language and geography, despite the lack of clear cut taxonomic structure in quadratted population data. For gene frequencies, these associations are more pronounced than for cranial measurements.

A summary ethnohistory database on population movements in Europe between 2000 B.C. and A.D. 1970 was related to genetic variances and distances based on 26 genetic systems. For the purposes of these analyses, Europe was divided into 85 terrestrial quadrats measuring 5° × 5°. Counts, stratified by time, were taken of the number of movements out of and into each quadrat (called source and target counts, respectively) and between each pair of quadrats. The source and target counts have distinct and different patterns in Europe and vary significantly over time. Central Europe and the Pontic area have the quadrats with the highest source counts, and the Balkans have the highest target counts. Modern genetic variances per quadrat are significantly correlated with source and target counts, somewhat more prominently with source counts. Genetic distances between pairs of quadrats are correlated strongly with geographic distances and moderately and negatively correlated with the total number of movements between these quadrats. Partial correlations of genetic distances with total number of movements, holding geographic distance constant, are small and mostly nonsignificant. These results are interpreted in light of our knowledge of the history and biology of the populations concerned.

In this study we relate language differences on a global scale with genetic distances for the same populations. The analysis is carried out on more populations (130) but fewer genetic systems (11) than earlier studies. We constructed an overall genetic distance matrix that allowed for missing values. A separate genetic distance matrix was also computed for each genetic system, and matching matrices of linguistic and geographic distances were associated with each genetic distance matrix, because the number of populations used differed among the genetic systems studied. Significant matrix correlations between language and genetics were found for both overall genetic distances and a substantial number of genetic systems, even when the effects of geographic distances were held constant. This demonstrates a significant correspondence between genetics and language on a global scale. Genetic matrices were correlated with two different linguistic distance matrices: one with higher (supraphyletic) taxonomic structure, in which among other features sub-Saharan Africans separate from non-Africans in the basal split and the Eurasiatic superphylum is postulated; and one without such structure. The correlations yield no genetic evidence to support the proposed higher linguistic structure. UPGMA and neighbor-joining trees were constructed for linguistic and genetic data. The proposed African split pattern is not supported by these data. Both types of trees indicate a pattern of grouping of east Asians, Arctic populations, and Australian natives separating from Caucasoid and African populations.

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 2216 1° × 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.

Synthetic maps of human gene frequencies, which are maps of principal component scores based on correlations of interpolated surfaces, have been popularized widely by L. Cavalli-Sforza, P. Menozzi, and A. Piazza. Such maps are used to make ethnohistorical inferences or to support various demographic or historical hypotheses. We show from first principles and by analyses of real and simulated data that synthetic maps are subject to large errors and that apparent geographic trends may be detected in spatially random data. We conclude that results featured as synthetic maps should be approached with considerable caution.