Bayesian Phylogenetics Methods, Algorithms, and Applications

About this book

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Computational Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research.

Encouraging interdisciplinary research, this book introduces state-of-the-art phylogenetics to the Bayesian statistical community and, likewise, presents state-of-the-art Bayesian statistics to the phylogenetics community. The book emphasizes model selection, reflecting recent interest in accurately estimating marginal likelihoods. It also discusses new approaches to improve mixing in Bayesian phylogenetic analyses in which the tree topology varies. In addition, Bayesian Phylogenetics: Methods, Computational Algorithms, and Applications covers divergence time estimation, biologically realistic models, and the burgeoning interface between phylogenetics and population genetics.

Contents

Bayesian phylogenetics: methods, computational algorithms, and applications
Introduction
Overview

Priors in Bayesian phylogenetics Ying Wang and Ziheng Yang
Introduction
Estimation of distance between two sequences
Priors on model parameters in Bayesian phylogenetics
Priors on the tree topology
Priors on times and rates for estimation of divergence times
Summary

IDR for marginal likelihood in Bayesian phylogenetics Serena Arima and Luca Tardella
Introduction
Substitution models: a brief overview
Bayesian model choice
Computational tools for Bayesian model evidence
Marginal likelihood for phylogenetic data
Discussion

Bayesian model selection in phylogenetics and genealogy-based population genetics Guy Baele and Philippe Lemey
Introduction
Prior and posterior-based estimators
Path sampling approaches
Simulation study: uncorrelated relaxed clocks
Practical example on demographic

Variable tree topology stepping-stone marginal likelihood estimation Mark T. Holder, Paul O. Lewis, David L. Swofford, and David Bryant
Introduction
The generalized stepping-stone (GSS) method
Reference distribution for tree topology
Example
Summary
Funding
Acknowledgements

Consistency of marginal likelihood estimation when topology varies Rui Wu, Ming-Hui Chen, Lynn Kuo, and Paul O. Lewis
Introduction
Notation and definitions
Empirical example
Discussion
Funding
Acknowledgements

Bayesian phylogeny analysis Sooyoung Cheon and Faming Liang
Introduction
Bayesian phylogeny inference
Monte Carlo methods for Bayesian phylogeny inference
Summary

Sequential Monte Carlo (SMC) for Bayesian phylogenetics Alexandre Bouchard-Côté
Using phylogenetic SMC samplers
How phylogenetic SMC works
Extensions and implementation issues
Discussion

Population model comparison using multi-locus datasets Michal Palczewski and Peter Beerli
Introduction
Bayesian inference of independent loci
Model comparison using our independent marginal likelihood sampler
Conclusion

Bayesian methods in the presence of recombination Mary K. Kuhner
Introduction to non-treelike phylogenies
Describing the ARG
Inference of the ARG
Mechanics of sampling ARGs
Hazards of Bayesian inference in the presence of recombination
Directions for future research
Open questions
Conclusions

Bayesian nonparametric phylodynamics Julia A. Palacios, Mandev S. Gill, Marc A. Suchard, and Vladimir N. Minin
Introduction
General model formulation
Priors on effective population size trajectory
Examples
Extensions and future directions

Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution Asger Hobolth and Jeffrey L. Thorne
Introduction
Independent sites models and summary statistics
Dependent{site models and Markov chain Monte Carlo
Future directions for sequence paths with dependence models

Bayesian inference of species divergence times Tracy A. Heath and Brian R. Moore
Introduction
Priors on branch rates
Priors on node times
Priors for calibrating divergence times
Practical issues for estimating divergence times
Summary and prospectus

Index

Customer Reviews

Biography

Ming-Hui Chen is a professor of statistics and director of the Statistical Consulting Services at the University of Connecticut. He was the recipient of the 2013 American Association of the University Professors Research Excellence Award, the 2013 College of Liberal Arts and Sciences Excellence in Research Award in the Physical Sciences Division at the University of Connecticut, and the 2011 International Chinese Statisticians Association (ICSA) Outstanding Service Award. An elected fellow of the ASA and the IMS, Dr. Chen has served on numerous professional committees, including the 2013 president of the ICSA, the 2011-2013 board of directors of the International Society for Bayesian Analysis, the 2007-2010 executive director of the ICSA, and the 2004-2006 board of directors of the ICSA. He has also served on editorial boards of Bayesian Analysis, Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Lifetime Data Analysis, Sankhya, and Statistics and Its Interface. His research interests include Bayesian statistical methodology, Bayesian computation, Bayesian phylogenetics, categorical data analysis, design of Bayesian clinical trials, DNA microarray data analysis, meta-analysis, missing data analysis, Monte Carlo methodology, prior elicitation, statistical methodology and analysis for prostate cancer data, and survival data analysis.

Lynn Kuo is a professor of statistics at the University of Connecticut. An elected fellow of the ASA, she was previously a research fellow in the Statistical Survey Institute at the USDA and at the Statistical and Applied Mathematical Sciences Institute (SAMSI). Dr. Kuo received an outstanding service award from ICSA in 2013 and was the secretary and treasurer of the Section of Bayesian Statistics of the ASA in 1998-1999. She has been an associate editor of the Journal of American Statistical Association and Naval Research Logistics and has served on many review panels for the CDC, NIH, and NSF. She has published more than 80 papers in numerous journals, including Systematic Biology, Molecular Biology and Evolution, Nature Genetics, and Statistics in Biosciences. Her research areas include nonparametric Bayesian statistics, survey sampling, survival analysis, longitudinal data analysis, Bayesian phylogenetics, and "omics" data analysis.

Paul O. Lewis is an associate professor of ecology and evolutionary biology and co-director of the Bioinformatics Facility in the Biotechnology/Bioservices Center at the University of Connecticut. His postdoctoral training was under Bruce S. Weir in the Department of Statistics at North Carolina State University and under David L. Swofford at the Smithsonian Institution Laboratory of Molecular Systematics. Dr. Lewis has been an associate editor of Systematic Biology and is the elected president of the Society of Systematic Biologists for 2015. His research interests include maximum likelihood and Bayesian methods in phylogenetics and the systematic evolution of green plants from green algae to angiosperms.