Indo‐European origins: A computer‐simulation test of five hypotheses

Abstract

Allele frequency distributions were generated by computer simulation of five models of microevolution in European populations. Genetic distances calculated from these distributions were compared with observed genetic distances among Indo-European speakers. The simulated models differ in complexity, but all incorporate random genetic drift and short-range gene flow (isolation by distance). The best correlations between observed and simulated data were obtained for two models where dispersal of Neolithic farmers from the Near East depends only on population growth. More complex models, where the timing of the farmers' expansion is constrained by archaeological time data, fail to account for a larger fraction of the observed genetic variation; this is also the case for a model including late Neolithic migrations from the Pontic steppes. The genetic structure of current populations speaking Indo-European languages seems therefore to largely reflect a Neolithic expansion. This is consistent with the hypothesis of a parallel spread of farming technologies and a proto-Indo-European language in the Neolithic. Allele-frequency gradients among Indo-European speakers may be due either to incomplete admixture between dispersing farmers, who presumably spoke proto-Indo-European, and pre-existing hunters and gatherers (as in the traditional demic diffusion hypothesis), or to founder effects during the farmers' dispersal. By contrast, successive migrational waves from the East, if any, do not seem to have had genetic consequences detectable by the present comparison of observed and simulated allele frequencies.

Y genetic data support the Neolithic demic diffusion model

“…1b). These values are in agreement with previous estimates obtained in simulation studies, showing that, to generate gradients similar to those observed in proteins, the genetic contribution of Neolithic farmers had to be between 65 and 100% (34,35).…”

Vertical cultural transmission effects on demic front propagation: Theory and application to the Neolithic transition in Europe

“…Before presenting this model, let us briefly describe the other two mechanisms mentioned above. A mathematical model of the third mechanism (horizontal cultural transmission [27,28,31,35]) will not be tackled in this paper because it would require substantial additional calculations since (i) it is well known from empirical data that human individuals often use the frequency of a trait as an indirect evaluation of its merit (biased transmission [26]), and (ii) horizontal transmission takes place at much longer distances than vertical transmission, even for pre-industrial populations [36], and this will lead to a distance-dependent probability of transmission. Finally, the fourth mechanism (local innovation) will not be analyzed here either, for the following reasons: (i) such local processes are probably not amenable to mathematical modeling, and (ii) a gradual front propagation in the Southeast-Northwest direction, with fairly constant speed, is observed for the Neolithic transition in Europe at the continental scale according to the archaeological data [37,38], thus, local innovations probably did not have an important effect on the global spread rate.…”

“…2 were also applied to simulate vertical cultural transmission in Ref. [54], but such terms were taken from a horizontal transmission model [35] and are not valid for vertical transmission, according to the present paper.…”

Population Migration and the Variation of Dopamine D4 Receptor (DRD4) Allele Frequencies Around the Globe

“…Therefore, we need a newer method that does not require one to make specific assumptions about the particular causes of linkages or commonalities among societies. Krackhardt (1988) recently proposed a newer network regression model based on a variant of the quadratic assignment procedure (Hubert and Schultz 1976;Mantel 1967), a standard approach in dealing with bivariate analysis in biological anthropology (Barbujani et al 1996;Relethford and Crawford 1995). Krackhardt's network regression extends the bivariate approach to allow for a multivariate regression model.…”