Inferring Population-Size History from Large Samples of Genome-Wide Molecular Data: An Approximate Bayesian Computation Approach

Molecular data sampled from present-day surviving individuals can contain considerable information about their demographic history. In particular, one classical question in population genetics is to reconstruct past population-size changes from such data. Relating these changes to various climatic, geological, or anthropogenic events allows characterizing the main factors driving genetic diversity and can influence major outcomes for conservation.

Until recently, mostly very simple histories, including one or two population-size changes, could be estimated from genetic data. These studies are now changing with the sequencing of entire genomes of many species, and several methods now allow one to infer complex histories consisting of several tens of population-size changes. However, analyzing entire genomes, while accounting for recombination, remains a statistical and numerical challenge. These methods, therefore, can only be applied to small samples with a few diploid genomes.

 Authors [ref below] have overcome this limitation by using an approximate estimation (Bayesian computational) approach, by which observed genomes are summarized using a small number of statistics related to allelic frequencies and linkage disequilibrium. In contrast to previous approaches, authors show herein that their method allows one to reconstruct also the most recent part (i.e. the last 100 generations) of a population-size history. As an illustration of their approach, interestingly, the authors apply it to large samples of whole-genome sequences in four breeds of cattle.

 PLoS Genet  2o16; 12: e1005877

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