Inferring Demography and Selection in Organisms Characterized by Skewed Offspring Distributions

Genetics
Andrew M SackmanJeffrey D Jensen

Abstract

The recent increase in time-series population genomic data from experimental, natural, and ancient populations has been accompanied by a promising growth in methodologies for inferring demographic and selective parameters from such data. However, these methods have largely presumed that the populations of interest are well-described by the Kingman coalescent. In reality, many groups of organisms, including viruses, marine organisms, and some plants, protists, and fungi, typified by high variance in progeny number, may be best characterized by multiple-merger coalescent models. Estimation of population genetic parameters under Wright-Fisher assumptions for these organisms may thus be prone to serious mis-inference. We propose a novel method for the joint inference of demography and selection under the Ψ-coalescent model, termed Multiple-Merger Coalescent Approximate Bayesian Computation, or MMC-ABC. We first demonstrate mis-inference under the Kingman, and then exhibit the superior performance of MMC-ABC under conditions of skewed offspring distributions. In order to highlight the utility of this approach, we reanalyzed previously published drug-selection lines of influenza A virus. We jointly inferred the extent of progeny-skew...Continue Reading

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Citations

Apr 2, 2020·Evolution; International Journal of Organic Evolution·Ana Y Morales-ArceJeffrey D Jensen
May 13, 2020·Genome Biology and Evolution·Rebecca B Harris, Jeffrey D Jensen
Sep 19, 2019·Virus Evolution·Patrick Hoscheit, Oliver G Pybus
Sep 2, 2020·Annual Review of Genetics·Bjarki Eldon

Related Concepts

Orthomyxovirus Type A, Porcine
Genetic Polymorphism
Selection, Genetic
Truncation Biases
Evolution, Molecular
Drug Resistance, Viral
Mitomycin
Fungi
Genome
Joints

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