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 genome-wide datasets,
mostly in human populations. Nevertheless, complex admixture histories,
beyond one or two pulses of admixture, remain methodologically
challenging to reconstruct. We develop 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; and draw model-parameter values from
prior distributions set by the user. For each simulation, MetHis
calculates 24 summary-statistics describing genetic diversity and
moments of individual admixture fractions. We coupled MetHis with
existing machine-learning ABC algorithms and investigate the admixture
history of hybrid populations. Results show that Random-Forest ABC
scenario-choice can accurately distinguish most complex admixture
scenarios and errors are mainly found in regions of the parameter space
where scenarios are highly nested, and, thus, biologically similar. We
focus on African American and Barbadian populations as case studies. We
find that Neural-Network ABC posterior parameter estimation is accurate
and reasonably conservative under complex admixture scenarios. For both
admixed populations, we find that monotonically decreasing contributions
over time, from Europe and Africa, explain the observed data more
accurately than multiple admixture pulses. This approach will allow for
reconstructing detailed admixture histories when maximum-likelihood
methods are intractable.