The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees

Author:

Lin Qianying1ORCID,Goldberg Emma E1,Leitner Thomas1ORCID,Molina-París Carmen1ORCID,King Aaron A2345ORCID,Romero-Severson Ethan O1ORCID

Affiliation:

1. Theoretical Biology and Biophysics Group, Los Alamos National Laboratory , Los Alamos, NM , USA

2. Department of Ecology & Evolutionary Biology, University of Michigan , Ann Arbor, MI , USA

3. Department of Mathematics, University of Michigan , Ann Arbor, MI , USA

4. Center for the Study of Complex Systems, University of Michigan , Ann Arbor, MI , USA

5. Santa Fe Institute , Santa Fe, NM , USA

Abstract

Abstract Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the “first-infection tree,” a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

Funder

National Institutes of Health

National Science Foundation/National Institutes of Health Interface Program

Publisher

Oxford University Press (OUP)

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