Bursts of coalescence within population pedigrees whenever big families occur

Author:

Diamantidis Dimitrios1,Fan Wai-Tong (Louis)12,Birkner Matthias3,Wakeley John2

Affiliation:

1. Department of Mathematics, Indiana University , Bloomington, IN 47405 , USA

2. Department of Organismic and Evolutionary Biology, Harvard University , Cambridge, MA 02138 , USA

3. Institut für Mathematik, Johannes-Gutenberg-Universität , 55099 Mainz , Germany

Abstract

Abstract We consider a simple diploid population-genetic model with potentially high variability of offspring numbers among individuals. Specifically, against a backdrop of Wright–Fisher reproduction and no selection, there is an additional probability that a big family occurs, meaning that a pair of individuals has a number of offspring on the order of the population size. We study how the pedigree of the population generated under this model affects the ancestral genetic process of a sample of size two at a single autosomal locus without recombination. Our population model is of the type for which multiple-merger coalescent processes have been described. We prove that the conditional distribution of the pairwise coalescence time given the random pedigree converges to a limit law as the population size tends to infinity. This limit law may or may not be the usual exponential distribution of the Kingman coalescent, depending on the frequency of big families. But because it includes the number and times of big families, it differs from the usual multiple-merger coalescent models. The usual multiple-merger coalescent models are seen as describing the ancestral process marginal to, or averaging over, the pedigree. In the limiting ancestral process conditional on the pedigree, the intervals between big families can be modeled using the Kingman coalescent but each big family causes a discrete jump in the probability of coalescence. Analogous results should hold for larger samples and other population models. We illustrate these results with simulations and additional analysis, highlighting their implications for inference and understanding of multilocus data.

Funder

National Science Foundation

Office of Naval Research

Publisher

Oxford University Press (OUP)

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