Complex genetic admixture histories reconstructed with Approximate Bayesian Computations

Abstract

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 dense genome-wide datasets mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct, especially when large datasets are unavailable. We develop an Approximate Bayesian Computations (ABC) framework to reconstruct complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs in a two-way admixed population for scenarios with multiple admixture pulses, or monotonically decreasing or increasing admixture at each generation; drawing model-parameter values from prior distributions set by the user. For each simulated dataset, we calculate 24 summary statistics describing genetic diversity and moments of individual admixture fraction. We coupled MetHis with existing ABC algorithms and investigate the admixture history of an African American and a Barbadian population. Results show that Random-Forest ABC scenario-choice, followed by Neural-Network ABC posterior parameter estimation, can distinguish most complex admixture scenarios and provide accurate model-parameter estimations. For both admixed populations, we find that monotonically decreasing contributions over time, from the European and African sources, explain the observed data more accurately than multiple admixture pulses. Furthermore, we find contrasted trajectories of introgression decay from the European and African sources between the two admixed populations. This approach will allow for reconstructing detailed admixture histories in numerous populations and species, particularly when maximum-likelihood methods are intractable.

Creating artificial human genomes using generative neural networks

“…They can be utilized as input for downstream operations such as forward steps of a specific evolutionary process for which they can become variations of the real datasets (similar to bootstrap), or they can be the sole input when the real dataset is not accessible. Initializing a simulation with real data is a procedure that is commonly used in population genetics [48,49]. Our results showed that GAN AGs are possibly underfitting while, on the contrary, RBM AGs are overfitting, based on distribution of minimum distance to the closest neighbour (S8 Fig) and AA TS scores (S13A Fig), although we showed how overfitting could be restrained by integrating AA TS scores within our models as a criterion for early stopping in training (before the networks start overfitting) and by modifying the RBM sampling scheme.…”

Anthropological genetics perspectives on the transatlantic slave trade

“…Several population genetics methods have been developed during the past 10 years to infer, from genomic data only, how many recurring or more temporary events of admixture, and their associated timings and respective intensities, have shaped the observed genetic patterns in admixed populations today (41)(42)(43).…”

Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde

“…Admixture-gene flow between previously diverged populations to form a new population with ancestry from both source populations-provides a particularly rapid opportunity for selection to act in a population by introducing alleles previously adapted in a source population into the admixed population (Huerta-Sánchez et al, 2014;Jeong et al, 2014;Norris et al, 2020;Racimo et al, 2015). Additionally, in recent human admixture, ancestry contributions from each source population are often large enough to introduce alleles at intermediate frequencies, potentially avoiding loss from drift (V. Fernandes et al, 2019;Fortes-Lima et al, 2019;Mathias et al, 2016;Ruiz-Linares et al, 2014). More generally, admixture is ubiquitous in human history (G. B.…”