Across language families: Genome diversity mirrors linguistic variation within Europe

Abstract

Objectives: The notion that patterns of linguistic and biological variation may cast light on each other and on population histories dates back to Darwin's times; yet, turning this intuition into a proper research program has met with serious methodological difficulties, especially affecting language comparisons. This article takes advantage of two new tools of comparative linguistics: a refined list of Indo-European cognate words, and a novel method of language comparison estimating linguistic diversity from a universal inventory of grammatical polymorphisms, and hence enabling comparison even across different families. We corroborated the method and used it to compare patterns of linguistic and genomic variation in Europe.

Materials and methods: Two sets of linguistic distances, lexical and syntactic, were inferred from these data and compared with measures of geographic and genomic distance through a series of matrix correlation tests. Linguistic and genomic trees were also estimated and compared. A method (Treemix) was used to infer migration episodes after the main population splits.

Results: We observed significant correlations between genomic and linguistic diversity, the latter inferred from data on both Indo-European and non-Indo-European languages. Contrary to previous observations, on the European scale, language proved a better predictor of genomic differences than geography. Inferred episodes of genetic admixture following the main population splits found convincing correlates also in the linguistic realm.

Discussion: These results pave the ground for previously unfeasible cross-disciplinary analyses at the worldwide scale, encompassing populations of distant language families.

Keywords: genome-wide diversity; human evolutionary history; parametric comparison method; single-nucleotide polymorphisms.

Figure 1

Geographic distribution of the samples considered in this study. Indo‐European‐speaking populations in blue, populations speaking Finno‐Ugric languages (Hungarian, Finnish) and the linguistic isolate (Basque) in red.

Figure 2

UPGMA trees summarizing population relationships. Distances inferred from: (A) lexical and (B) syntactic comparisons among 12 Indo‐European‐speaking European populations; (C) syntactic comparisons among 15 European languages, and (D) F _ST distances among 15 populations sharing 177,949 SNPs. Lexical distances were estimated from lists of cognate words, amounting to over 6,000 roots (http://ielex.mpi.nl/); syntactic distances were measured over 56 parameters of nominal phrases (http://dx.doi.org/10.1075/jhl.3.1.07lon.additional). In (D), numbers indicate the support of the branching after 100 bootstrap replicates. The matrix perturbation techniques usable to test the robustness of trees (bootstrapping and jackknifing) provide stable topologies, but owing to the small number of characters involved they are only relatively reliable (cf. Longobardi et al., 2013 for more details). Therefore, bootstrapping scores have been only reported here for the genetic tree D.

Figure 3

Projection on two dimensions of the main components (PCA) of linguistic (A) and individual genomic (B) variation. The linguistic PCA was performed using the R FactoMineR program, with neutralized parameter values coded as “NA,” whereas the genomic PCA was calculated with the R SNPRelate package (Lê et al., 2008). Note that the linguistic scatter diagram accounts for a fraction of the total variance that is >25‐fold as large as that accounted for by the genomic scatter diagram.

Figure 4

Unsupervised ancestry‐inference analysis based on the software ADMIXTURE. Each individual genotype is represented by a column in the area representing the appropriate population, and colors correspond to the fraction of the genotype that can be attributed to each of the K groups (2 ≤ K ≤ 5) assumed to have contributed to the populations' ancestry.

Figure 5

Maximum‐likelihood population trees. The algorithm chosen, TreeMix (28), estimates phylogenetic relationships with (A) three, (B) one, and (C) two superimposed migration events after the main population splits.