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      Local Population Structure and Patterns of Western Hemisphere Dispersal for Coccidioides spp., the Fungal Cause of Valley Fever

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          ABSTRACT

          Coccidioidomycosis (or valley fever) is a fungal disease with high morbidity and mortality that affects tens of thousands of people each year. This infection is caused by two sibling species, Coccidioides immitis and C. posadasii, which are endemic to specific arid locales throughout the Western Hemisphere, particularly the desert southwest of the United States. Recent epidemiological and population genetic data suggest that the geographic range of coccidioidomycosis is expanding, as new endemic clusters have been identified in the state of Washington, well outside the established endemic range. The genetic mechanisms and epidemiological consequences of this expansion are unknown and require better understanding of the population structure and evolutionary history of these pathogens. Here we performed multiple phylogenetic inference and population genomics analyses of 68 new and 18 previously published genomes. The results provide evidence of substantial population structure in C. posadasii and demonstrate the presence of distinct geographic clades in central and southern Arizona as well as dispersed populations in Texas, Mexico, South America, and Central America. Although a smaller number of C. immitis strains were included in the analyses, some evidence of phylogeographic structure was also detected in this species, which has been historically limited to California and Baja, Mexico. Bayesian analyses indicated that C. posadasii is the more ancient of the two species and that Arizona contains the most diverse subpopulations. We propose a southern Arizona-northern Mexico origin for C. posadasii and describe a pathway for dispersal and distribution out of this region.

          IMPORTANCE

          Coccidioidomycosis, or valley fever, is caused by the pathogenic fungi Coccidioides posadasii and C. immitis. The fungal species and disease are primarily found in the American desert southwest, with spotted distribution throughout the Western Hemisphere. Initial molecular studies suggested a likely anthropogenic movement of C. posadasii from North America to South America. Here we comparatively analyze eighty-six genomes of the two Coccidioides species and establish local and species-wide population structures to not only clarify the earlier dispersal hypothesis but also provide evidence of likely ancestral populations and patterns of dispersal for the known subpopulations of C. posadasii.

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          Neighbor-net: an agglomerative method for the construction of phylogenetic networks.

          We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.
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            Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

            Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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              Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

              Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                26 April 2016
                Mar-Apr 2016
                : 7
                : 2
                : e00550-16
                Affiliations
                [a ]TGen North, Translational Genomics Research Institute, Flagstaff, Arizona, USA
                [b ]Informatics and Computing Center, Northern Arizona University, Flagstaff, Arizona, USA
                [c ]Mycotic Diseases Branch, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
                [d ]Division of Public Health Services, Arizona Department of Health Services, Phoenix, Arizona, USA
                [e ]Asociación de Salud Integral, Guatemala City, Guatemala
                [f ]Universidad de San Carlos, Ciudad Universitaria, Guatemala City, Guatemala
                [g ]Division of Infectious Diseases, Department of Medicine, University of California, Davis, Davis, California, USA
                [h ]Microbial Genetics and Genomics Center, Northern Arizona University, Flagstaff, Arizona, USA
                Author notes
                Address correspondence to David M. Engelthaler, dengelthaler@ 123456tgen.org .

                D. M. E. and C. C. R. contributed equally to this article.

                Editor Joseph Heitman, Duke University

                Author information
                http://orcid.org/0000-0002-3439-4517
                Article
                mBio00550-16
                10.1128/mBio.00550-16
                4850269
                27118594
                b49121f7-231b-4c40-b7b0-770a711bfae9
                Copyright © 2016 Engelthaler et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 29 March 2016
                : 31 March 2016
                Page count
                supplementary-material: 10, Figures: 6, Tables: 2, Equations: 0, References: 67, Pages: 15, Words: 11929
                Funding
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) http://dx.doi.org/10.13039/100000060
                Award ID: R21AA076773
                Award Recipient : Paul Keim
                Funded by: HHS | Centers for Disease Control and Prevention (CDC) http://dx.doi.org/10.13039/100000030
                Award ID: 200201461029
                Award Recipient : David M. Engelthaler
                Funded by: ADHS | Arizona Biomedical Research Commission (ABRC) http://dx.doi.org/10.13039/100008335
                Award ID: 20080816
                Award Recipient : Paul Keim
                Categories
                Research Article
                Custom metadata
                March/April 2016

                Life sciences
                Life sciences

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