Hierarchical Phylogenetic Models for Analyzing Multipartite Sequence Data (original) (raw)

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1

Department of Biomathematics, David Geffen School of Medicine, University of California–Los Angeles

Los Angeles, California 90095–1766, USA

E-mail: msuchard@ucla.edu

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2

Department of Biostatistics, School of Public Health, University of California–Los Angeles

Los Angeles, California 90095–1772, USA

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,

1

Department of Biomathematics, David Geffen School of Medicine, University of California–Los Angeles

Los Angeles, California 90095–1766, USA

E-mail: msuchard@ucla.edu

2

Department of Biostatistics, School of Public Health, University of California–Los Angeles

Los Angeles, California 90095–1772, USA

3

Department of Human Genetics, David Geffen School of Medicine, University of California–Los Angeles

Los Angeles, California 90095–1766, USA

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2

Department of Biostatistics, School of Public Health, University of California–Los Angeles

Los Angeles, California 90095–1772, USA

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Received:

09 December 2002

Revision received:

14 April 2003

Published:

01 October 2003

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Marc A. Suchard, Christina M. R. Kitchen, Janet S. Sinsheimer, Robert E. Weiss, Hierarchical Phylogenetic Models for Analyzing Multipartite Sequence Data, Systematic Biology, Volume 52, Issue 5, 1 October 2003, Pages 649–664, https://doi.org/10.1080/10635150390238879
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Abstract

Debate exists over how to incorporate information from multipartite sequence data in phylogenetic analyses. Strict combined-data approaches argue for concatenation of all partitions and estimation of one evolutionary history, maximizing the explanatory power of the data. Consensus/independence approaches endorse a two-step procedure where partitions are analyzed independently and then a consensus is determined from the multiple results. Mixtures across the model space of a strict combined-data approach and a priori independent parameters are popular methods to integrate these methods. We propose an alternative middle ground by constructing a Bayesian hierarchical phylogenetic model. Our hierarchical framework enables researchers to pool information across data partitions to improve estimate precision in individual partitions while permitting estimation and testing of tendencies in across-partition quantities. Such across-partition quantities include the distribution from which individual topologies relating the sequences within a partition are drawn. We propose standard hierarchical priors on continuous evolutionary parameters across partitions, while the structure on topologies varies depending on the research problem. We illustrate our model with three examples. We first explore the evolutionary history of the guinea pig (Cavia porcellus) using alignments of 13 mitochondrial genes. The hierarchical model returns substantially more precise continuous parameter estimates than an independent parameter approach without losing the salient features of the data. Second, we analyze the frequency of horizontal gene transfer using 50 prokaryotic genes. We assume an unknown species-level topology and allow individual gene topologies to differ from this with a small estimable probability. Simultaneously inferring the species and individual gene topologies returns a transfer frequency of 17%. We also examine HIV sequences longitudinally sampled from HIV+ patients. We ask whether posttreatment development of CCR5 coreceptor virus represents concerted evolution from middisease CXCR4 virus or reemergence of initial infecting CCR5 virus. The hierarchical model pools partitions from multiple unrelated patients by assuming that the topology for each patient is drawn from a multinomial distribution with unknown probabilities. Preliminary results suggest evolution and not reemergence.

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© 2003 Society of Systematic Biologists

Associate Editor: Ziheng Yang

Ziheng Yang

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