Comparative performance of viral landscape phylogeography approaches

• 成果类型: Article
• 署名作者: Dellicour, Simon; Gambaro, Fabiana; Jacquot, Maude; Lequime, Sebastian; Baele, Guy; Gilbert, Marius; Pybus, Oliver G.; Suchard, Marc A.; Lemey, Philippe
• 署名单位: Universite Libre de Bruxelles; KU Leuven; Vrije Universiteit Brussel; Universite Libre de Bruxelles; Ifremer; University of Groningen; University of Oxford; University of London; University of London Royal Veterinary College; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10098
• 发表日期: 2025-07-01
• 关键词: genetics; inference; EPIDEMIC; DYNAMICS; MODEL; expansion; EVOLUTION; ecology

摘要

The rapid evolution of RNA viruses implies that their evolutionary and ecological processes occur on the same time scale. Genome sequences of these pathogens therefore can contain information about the processes that govern their transmission and dispersal. Landscape phylogeographic approaches use phylogeographic reconstructions to investigate the impact of environmental factors and variables on the spatial spread of viruses. Here, we extend and improve existing approaches and develop three novel landscape phylogeographic methods that can test the impact of continuous environmental factors on the diffusion velocity of viral lineages. In order to evaluate the different methods, we also implemented two simulation frameworks to test and compare their statistical performance. The results enable us to formulate clear guidelines for the use of three complementary landscape phylogeographic approaches that have sufficient statistical power and low rates of false positives. Our open- source methods are available to the cientific community and can be used to investigate the drivers of viral spread, with potential benefits for understanding virus epidemiology and designing tailored intervention strategies.