Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2

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Abstract

Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts.

Abstract

Spatiotemporal sampling gaps in existing pathogen genomic data limits their use in understanding epidemiological patterns. Here, the authors apply a phylogeographic approach with SARS-CoV-2 genomes to accurately reproduce pathogen spread by accounting for spatial biases and travel history of the individual.

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Most cited references 38

Record: found
Abstract: found
Article: not found

Minimap2: pairwise alignment for nucleotide sequences

Heng Li (2018)

Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.

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Record: found
Abstract: found
Article: found

Is Open Access

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

Bui Minh, Heiko Schmidt, Olga Chernomor … (2020)

Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

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Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

Andrew Rambaut, Alexei J. Drummond, Dong Xie … (2018)

Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.

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Author and article information

Contributors

Philippe Lemey:

ORCID: http://orcid.org/0000-0003-2826-5353

philippe.lemey@kuleuven.be

Marc A. Suchard:

ORCID: http://orcid.org/0000-0001-9818-479X

msuchard@ucla.edu

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 9 October 2020

Publication date PMC-release: 9 October 2020

Publication date Collection: 2020

Volume: 11

Electronic Location Identifier: 5110

Affiliations

[1 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, , Laboratory of Clinical and Evolutionary Virology, ; Leuven, Belgium

[2 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Institute of Evolutionary Biology, , University of Edinburgh, ; Edinburgh, EH9 3FL UK

[3 ]INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France

[4 ]Department of Immunology and Microbiology, Scripps Research, La Jolla, CA 92037 USA

[5 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, Department of Ecology and Evolutionary Biology, , University of Arizona, ; Tucson, AZ 85721 USA

[6 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Division of International Epidemiology and Population Studies, Fogarty International Center, , National Institutes of Health, ; Bethesda, MD 20892 USA

[7 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Biomathematics, David Geffen School of Medicine, , University of California Los Angeles, ; Los Angeles, CA 90095 USA

[8 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Biostatistics, Fielding School of Public Health, , University of California Los Angeles, ; Los Angeles, CA 90095 USA

[9 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Human Genetics, David Geffen School of Medicine, , University of California Los Angeles, ; Los Angeles, CA 90095 USA

Author information

Philippe Lemey http://orcid.org/0000-0003-2826-5353

Samuel L. Hong http://orcid.org/0000-0001-6354-4943

Verity Hill http://orcid.org/0000-0002-3509-8146

Vittoria Colizza http://orcid.org/0000-0002-2113-2374

Áine O’Toole http://orcid.org/0000-0001-8083-474X

Andrew Rambaut http://orcid.org/0000-0003-4337-3707

Marc A. Suchard http://orcid.org/0000-0001-9818-479X

Article

Publisher ID: 18877

DOI: 10.1038/s41467-020-18877-9

PMC ID: 7547076

PubMed ID: 33037213

SO-VID: 4b5ff67b-bc9c-4727-9bc4-79032ee4a2e4

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 13 June 2020

Date accepted : 17 September 2020

Funding

Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);

Award ID: 874850