Dataset Information

Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations.

ABSTRACT: Linguistic and genetic data have been widely compared, but the histories underlying these descriptions are rarely jointly inferred. We developed a unique methodological framework for analysing jointly language diversity and genetic polymorphism data, to infer the past history of separation, exchange and admixture events among human populations. This method relies on approximate Bayesian computations that enable the identification of the most probable historical scenario underlying each type of data, and to infer the parameters of these scenarios. For this purpose, we developed a new computer program PopLingSim that simulates the evolution of linguistic diversity, which we coupled with an existing coalescent-based genetic simulation program, to simulate both linguistic and genetic data within a set of populations. Applying this new program to a wide linguistic and genetic dataset of Central Asia, we found several differences between linguistic and genetic histories. In particular, we showed how genetic and linguistic exchanges differed in the past in this area: some cultural exchanges were maintained without genetic exchanges. The methodological framework and the linguistic simulation tool developed here can be used in future work for disentangling complex linguistic and genetic evolutions underlying human biological and cultural histories.

SUBMITTER: Thouzeau V

PROVIDER: S-EPMC5577475 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations.

Thouzeau Valentin V Mennecier Philippe P Verdu Paul P Austerlitz Frédéric F

Proceedings. Biological sciences 20170801 1861

Linguistic and genetic data have been widely compared, but the histories underlying these descriptions are rarely jointly inferred. We developed a unique methodological framework for analysing jointly language diversity and genetic polymorphism data, to infer the past history of separation, exchange and admixture events among human populations. This method relies on approximate Bayesian computations that enable the identification of the most probable historical scenario underlying each type of d ...[more]

PMID: 28835553

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Project description:Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios. MetHis allows users to draw model-parameter values from prior distributions set by the user, and, for each simulation, MetHis can calculate numerous summary statistics describing genetic diversity patterns and moments of the distribution of individual admixture fractions. We coupled MetHis with existing machine-learning ABC algorithms and investigated the admixture history of admixed populations. Results showed that random forest ABC scenario-choice could accurately distinguish among most complex admixture scenarios, and errors were mainly found in regions of the parameter space where scenarios were highly nested, and, thus, biologically similar. We focused on African American and Barbadian populations as two study-cases. We found that neural network ABC posterior parameter estimation was accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we found that monotonically decreasing contributions over time, from Europe and Africa, explained the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum-likelihood methods are intractable.

Dataset Information

Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations.

Publications

Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations.

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