Distinguishing among complex evolutionary models using unphased whole-genome data through random forest approximate Bayesian computation.

Autor: Ghirotto S; Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy., Vizzari MT; Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy., Tassi F; Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy., Barbujani G; Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy., Benazzo A; Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy.
Jazyk: angličtina
Zdroj: Molecular ecology resources [Mol Ecol Resour] 2021 Nov; Vol. 21 (8), pp. 2614-2628. Date of Electronic Publication: 2020 Oct 25.
DOI: 10.1111/1755-0998.13263
Abstrakt: Inferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available should in principle facilitate this inference. In practice, however, the available inferential methods suffer from severe limitations. Although hundreds complete genomes can be simultaneously analysed, complex demographic processes can easily exceed computational constraints, and the procedures to evaluate the reliability of the estimates contribute to increase the computational effort. Here we present an approximate Bayesian computation framework based on the random forest algorithm (ABC-RF), to infer complex past population processes using complete genomes. To this aim, we propose to summarize the data by the full genomic distribution of the four mutually exclusive categories of segregating sites (FDSS), a statistic fast to compute from unphased genome data and that does not require the ancestral state of alleles to be known. We constructed an efficient ABC pipeline and tested how accurately it allows one to recognize the true model among models of increasing complexity, using simulated data and taking into account different sampling strategies in terms of number of individuals analysed, number and size of the genetic loci considered. We also compared the FDSS with the unfolded and folded site frequency spectrum (SFS), and for these statistics we highlighted the experimental conditions maximizing the inferential power of the ABC-RF procedure. We finally analysed real data sets, testing models on the dispersal of anatomically modern humans out of Africa and exploring the evolutionary relationships of the three species of Orangutan inhabiting Borneo and Sumatra.
(© 2020 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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