1
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Gallego-García P, Hong SL, Bollen N, Dellicour S, Baele G, Suchard MA, Lemey P, Posada D. Dispersal history of SARS-CoV-2 variants Alpha, Delta, and Omicron (BA.1) in Spain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.01.24309632. [PMID: 39006420 PMCID: PMC11245079 DOI: 10.1101/2024.07.01.24309632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Different factors influence the spread of SARS-CoV-2, from the inherent transmission capabilities of the different variants to the control measurements put in place. Here we studied the introduction of the Alpha, Delta, and Omicron-BA.1 variants of concern (VOCs) into Spain. For this, we collected genomic data from the GISAID database and combined it with connectivity data from different countries with Spain to perform a phylodynamic Bayesian analysis of the introductions. Our findings reveal that the introductions of these VOCs predominantly originated from France, especially in the case of Alpha. As travel restrictions were eased during the Delta and Omicron-BA.1 waves, the number of introductions from distinct countries increased, with the United Kingdom and Germany becoming significant sources of the virus. The largest number of introductions detected corresponded to the Delta wave, which was associated with fewer restrictions and the summer period, when Spain receives a considerable number of tourists. This research underscores the importance of monitoring international travel patterns and implementing targeted public health measures to manage the spread of SARS-CoV-2.
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Affiliation(s)
- Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Samuel L. Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven – University of Leuven, 3000 Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Marc A. Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven – University of Leuven, 3000 Leuven, Belgium
- Global Virus Network (GVN), Baltimore, MD 21201, USA
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
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2
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Gallego-García P, Estévez-Gómez N, De Chiara L, Alvariño P, Juiz-González PM, Torres-Beceiro I, Poza M, Vallejo JA, Rumbo-Feal S, Conde-Pérez K, Aja-Macaya P, Ladra S, Moreno-Flores A, Gude-González MJ, Coira A, Aguilera A, Costa-Alcalde JJ, Trastoy R, Barbeito-Castiñeiras G, García-Souto D, Tubio JMC, Trigo-Daporta M, Camacho-Zamora P, Costa JG, González-Domínguez M, Canoura-Fernández L, Glez-Peña D, Pérez-Castro S, Cabrera JJ, Daviña-Núñez C, Godoy-Diz M, Treinta-Álvarez AB, Veiga MI, Sousa JC, Osório NS, Comas I, González-Candelas F, Hong SL, Bollen N, Dellicour S, Baele G, Suchard MA, Lemey P, Agulla A, Bou G, Alonso-García P, Pérez-Del-Molino ML, García-Campello M, Paz-Vidal I, Regueiro B, Posada D. Dispersal history of SARS-CoV-2 in Galicia, Spain. J Med Virol 2024; 96:e29773. [PMID: 38940448 DOI: 10.1002/jmv.29773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024]
Abstract
The dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron-BA.1 variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the United States became increasingly significant. The number of detected introductions varied from 96 and 101 for Alpha and Delta to 39 for Omicron-BA.1. Most of these introductions left a low number of descendants (<10), suggesting a limited impact on the evolution of the pandemic in Galicia. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.
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Affiliation(s)
- Pilar Gallego-García
- CINBIO, Universidade de Vigo, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Nuria Estévez-Gómez
- CINBIO, Universidade de Vigo, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Loretta De Chiara
- CINBIO, Universidade de Vigo, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo, Spain
| | | | - Pedro M Juiz-González
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, Ferrol, Spain
| | - Isabel Torres-Beceiro
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, Ferrol, Spain
| | - Margarita Poza
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
- Microbiome and Health Group, Faculty of Sciences, University of A Coruña (UDC), A Coruña, Spain
| | - Juan A Vallejo
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
| | - Soraya Rumbo-Feal
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
| | - Kelly Conde-Pérez
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
| | - Pablo Aja-Macaya
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
| | - Susana Ladra
- Database Laboratory, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), A Coruña, Spain
| | | | | | - Amparo Coira
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - Antonio Aguilera
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - José J Costa-Alcalde
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - Rocío Trastoy
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - Gema Barbeito-Castiñeiras
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - Daniel García-Souto
- CiMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - José M C Tubio
- CiMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics and Physic Anthropology, Santiago de Compostela, Spain
| | - Matilde Trigo-Daporta
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Pablo Camacho-Zamora
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Juan García Costa
- Servicio de Microbiología, Complejo Hospitalario Universitario de Ourense, Ourense, Spain
| | | | - Luis Canoura-Fernández
- Servicio de Microbiología, Complejo Hospitalario Universitario de Ourense, Ourense, Spain
| | - Daniel Glez-Peña
- CINBIO, Universidade de Vigo, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Sonia Pérez-Castro
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jorge J Cabrera
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Carlos Daviña-Núñez
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Montserrat Godoy-Diz
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo, Spain
| | - Ana Belén Treinta-Álvarez
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo, Spain
| | - Maria Isabel Veiga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's-PT Government Associate Laboratory, Guimarães, Portugal
| | - João Carlos Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's-PT Government Associate Laboratory, Guimarães, Portugal
| | - Nuno S Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's-PT Government Associate Laboratory, Guimarães, Portugal
| | - Iñaki Comas
- Tuberculosis Genomics Unit, BioMedicine Institute of Valencia, Spanish Research Council (CSIC), Valencia, Spain
- CIBER in Epidemiology and Public Health, Madrid, Spain
- Joint Research Unit "Infection and Public Health", FISABIO-University of Valencia, Valencia, Spain
| | - Fernando González-Candelas
- CIBER in Epidemiology and Public Health, Madrid, Spain
- Joint Research Unit "Infection and Public Health", FISABIO-University of Valencia, Valencia, Spain
- Institute for Integrative Systems Biology (I2SysBio), University of Valencia-CSIC, Valencia, Spain
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
- Global Virus Network (GVN), Baltimore, Maryland, USA
| | - Andrés Agulla
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, Ferrol, Spain
| | - Germán Bou
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA), University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Edificio Sur, Hospital Universitario A Coruña, As Xubias, A Coruña, Spain
| | - Pilar Alonso-García
- Servicio de Microbiología, Hospital Universitario Lucus Augusti, Lugo, Spain
| | - María Luisa Pérez-Del-Molino
- Department of Microbiology, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS), Santiago de Compostela, Spain
| | - Marta García-Campello
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Isabel Paz-Vidal
- Servicio de Microbiología, Complejo Hospitalario Universitario de Ourense, Ourense, Spain
| | - Benito Regueiro
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo, Spain
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3
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Shao Y, Magee AF, Vasylyeva TI, Suchard MA. Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models. PLoS Comput Biol 2024; 20:e1011640. [PMID: 38551979 PMCID: PMC11006205 DOI: 10.1371/journal.pcbi.1011640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/10/2024] [Accepted: 03/10/2024] [Indexed: 04/09/2024] Open
Abstract
Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
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Affiliation(s)
- Yucai Shao
- Department of Biostatistics, University of California, Los Angeles, California, United States of America
| | - Andrew F. Magee
- Department of Biomathematics, University of California, Los Angeles, California, United States of America
| | - Tetyana I. Vasylyeva
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Department of Population Health and Disease Prevention, University of California Irvine, Irvine, California, United States of America
| | - Marc A. Suchard
- Department of Biostatistics, University of California, Los Angeles, California, United States of America
- Department of Biomathematics, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, Universtiy of California, Los Angeles, California, United States of America
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4
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Gallego-García P, Estévez-Gómez N, De Chiara L, Alvariño P, Juiz-González PM, Torres-Beceiro I, Poza M, Vallejo JA, Rumbo-Feal S, Conde-Pérez K, Aja-Macaya P, Ladra S, Moreno-Flores A, Gude-González MJ, Coira A, Aguilera A, Costa-Alcalde JJ, Trastoy R, Barbeito-Castiñeiras G, García-Souto D, Tubio JMC, Trigo-Daporta M, Camacho-Zamora P, Costa JG, González-Domínguez M, Canoura-Fernández L, Glez-Peña D, Pérez-Castro S, Cabrera JJ, Daviña-Núñez C, Godoy-Diz M, Treinta-Álvarez AB, Veiga MI, Sousa JC, Osório NS, Comas I, González-Candelas F, Hong SL, Bollen N, Dellicour S, Baele G, Suchard MA, Lemey P, Agulla A, Bou G, Alonso-García P, Pérez-Del-Molino ML, García-Campello M, Paz-Vidal I, Regueiro B, Posada D. Dispersal history of SARS-CoV-2 in Galicia, Spain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.27.24303385. [PMID: 38463998 PMCID: PMC10925372 DOI: 10.1101/2024.02.27.24303385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The dynamics of SARS-CoV-2 transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the USA became increasingly significant. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.
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Affiliation(s)
- Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Nuria Estévez-Gómez
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Loretta De Chiara
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
| | | | - Pedro M Juiz-González
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, 15405 Ferrol
| | - Isabel Torres-Beceiro
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, 15405 Ferrol
| | - Margarita Poza
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
- Microbiome and Health Group, Faculty of Sciences, University of A Coruña (UDC). Campus da Zapateira, 15008, A Coruña, Spain
| | - Juan A Vallejo
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
| | - Soraya Rumbo-Feal
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
| | - Kelly Conde-Pérez
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
| | - Pablo Aja-Macaya
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
| | - Susana Ladra
- Database Laboratory, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 A Coruña, Spain
| | | | | | - Amparo Coira
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - Antonio Aguilera
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - José J Costa-Alcalde
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - Rocío Trastoy
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - Gema Barbeito-Castiñeiras
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - Daniel García-Souto
- CiMUS, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain. - Department of Zoology, Genetics and Physic Anthropology, 15782, Santiago de Compostela, Spain
| | - José M C Tubio
- CiMUS, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain. - Department of Zoology, Genetics and Physic Anthropology, 15782, Santiago de Compostela, Spain
| | - Matilde Trigo-Daporta
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Pablo Camacho-Zamora
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Juan García Costa
- Servicio de Microbiología. Complejo Hospitalario Universitario de Ourense, 32005, Ourense, Spain
| | - María González-Domínguez
- Servicio de Microbiología. Complejo Hospitalario Universitario de Ourense, 32005, Ourense, Spain
| | - Luis Canoura-Fernández
- Servicio de Microbiología. Complejo Hospitalario Universitario de Ourense, 32005, Ourense, Spain
| | - Daniel Glez-Peña
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Sonia Pérez-Castro
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo 36213, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Jorge J Cabrera
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo 36213, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Carlos Daviña-Núñez
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Montserrat Godoy-Diz
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo 36213, Spain
| | - Ana Belén Treinta-Álvarez
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo 36213, Spain
| | - Maria Isabel Veiga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal - ICVS/3B's-PT Government Associate Laboratory, 4806-909, Guimarães/ Braga, Portugal
| | - João Carlos Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal - ICVS/3B's-PT Government Associate Laboratory, 4806-909, Guimarães/ Braga, Portugal
| | - Nuno S Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal - ICVS/3B's-PT Government Associate Laboratory, 4806-909, Guimarães/ Braga, Portugal
| | - Iñaki Comas
- Tuberculosis Genomics Unit, Biomedicine Institute of Valencia, Spanish Research Council (CSIC), Valencia, Spain
- CIBER in Epidemiology and Public Health, Spain; Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain
| | - Fernando González-Candelas
- CIBER in Epidemiology and Public Health, Spain; Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain
- Institute for Integrative Systems Biology (I2SysBio), University of Valencia-CSIC, Valencia, Spain
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven - University of Leuven, 3000 Leuven, Belgium
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven - University of Leuven, 3000 Leuven, Belgium
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven - University of Leuven, 3000 Leuven, Belgium
- Spatial Epidemiology Lab, Université Libre de Bruxelles, 1000 Brussels, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven - University of Leuven, 3000 Leuven, Belgium
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA - Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA - Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, KU Leuven - University of Leuven, 3000 Leuven, Belgium
- Global Virus Network (GVN), Baltimore, MD 21201, USA
| | - Andrés Agulla
- Servicio de Microbiología del Complejo Hospitalario Universitario de Ferrol, 15405 Ferrol
| | - Germán Bou
- Microbiology Research Group, Institute of Biomedical Research (INIBIC) - Interdisciplinary Center for Chemistry and Biology (CICA) - University of A Coruña (UDC) - CIBER de Enfermedades Infecciosas (CIBERINFEC-ISCIII), Madrid. Servicio de Microbiología, 3° planta, Edificio Sur, Hospital Universitario A Coruña, As Xubias, 15006, A Coruña, Spain
| | - Pilar Alonso-García
- Servicio de Microbiología, Hospital Universitario Lucus Augusti, Lugo, Spain
| | - María Luisa Pérez-Del-Molino
- Microbiology Department, Complexo Hospitalario Universitario de Santiago de Compostela. SERGAS - Microbiology Research Group, Institute of Biomedical Research (IDIS) - Santiago de Compostela 15706, Spain
| | - Marta García-Campello
- Clinical Microbiology Unit, Complexo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
| | - Isabel Paz-Vidal
- Servicio de Microbiología. Complejo Hospitalario Universitario de Ourense, 32005, Ourense, Spain
| | - Benito Regueiro
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), SERGAS, Vigo 36213, Spain
- Microbiology and Infectology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
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5
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Gangavarapu K, Ji X, Baele G, Fourment M, Lemey P, Matsen FA, Suchard MA. Many-core algorithms for high-dimensional gradients on phylogenetic trees. Bioinformatics 2024; 40:btae030. [PMID: 38243701 PMCID: PMC10868298 DOI: 10.1093/bioinformatics/btae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 12/20/2023] [Accepted: 01/15/2024] [Indexed: 01/21/2024] Open
Abstract
MOTIVATION Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. RESULTS We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. AVAILABILITY AND IMPLEMENTATION We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).
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Affiliation(s)
- Karthik Gangavarapu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, United States
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Frederick A Matsen
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Department of Statistics, University of Washington, Seattle, WA, United States
- Department of Genome Sciences, University of Washington, Seattle, WA, United States
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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6
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Didier G, Glatt-Holtz NE, Holbrook AJ, Magee AF, Suchard MA. On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2. Proc Natl Acad Sci U S A 2024; 121:e2318989121. [PMID: 38215186 PMCID: PMC10801879 DOI: 10.1073/pnas.2318989121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/30/2023] [Indexed: 01/14/2024] Open
Abstract
The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary biology. Learning CTMC model parameters using modern, gradient-based methods requires the derivative of the matrix exponential evaluated at the CTMC's infinitesimal generator (rate) matrix. Motivated by the derivative's extreme computational complexity as a function of state space cardinality, recent work demonstrates the surprising effectiveness of a naive, first-order approximation for a host of problems in computational biology. In response to this empirical success, we obtain rigorous deterministic and probabilistic bounds for the error accrued by the naive approximation and establish a "blessing of dimensionality" result that is universal for a large class of rate matrices with random entries. Finally, we apply the first-order approximation within surrogate-trajectory Hamiltonian Monte Carlo for the analysis of the early spread of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across 44 geographic regions that comprise a state space of unprecedented dimensionality for unstructured (flexible) CTMC models within evolutionary biology.
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Affiliation(s)
- Gustavo Didier
- Department of Mathematics, Tulane University, New Orleans, LA70118
| | | | - Andrew J. Holbrook
- Department of Biostatistics, University of California, Los Angeles, CA90095
| | - Andrew F. Magee
- Department of Biostatistics, University of California, Los Angeles, CA90095
| | - Marc A. Suchard
- Department of Biostatistics, University of California, Los Angeles, CA90095
- Department of Biomathematics, University of California, Los Angeles, CA90095
- Department of Human Genetics, University of California, Los Angeles, CA90095
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7
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Fisher AA, Ji X, Nishimura A, Baele G, Lemey P, Suchard MA. Shrinkage-based Random Local Clocks with Scalable Inference. Mol Biol Evol 2023; 40:msad242. [PMID: 37950885 PMCID: PMC10665039 DOI: 10.1093/molbev/msad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/23/2023] [Accepted: 11/07/2023] [Indexed: 11/13/2023] Open
Abstract
Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.
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Affiliation(s)
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, USA
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8
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Shao Y, Magee AF, Vasylyeva TI, Suchard MA. Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.564882. [PMID: 37961423 PMCID: PMC10634968 DOI: 10.1101/2023.10.31.564882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
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Affiliation(s)
- Yucai Shao
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, United States
| | - Andrew F. Magee
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, United States
| | - Tetyana I. Vasylyeva
- Department of Medicine, University of California San Diego, La Jolla, United States
- Department of Population Health and Disease Prevention, University of California Irvine, Irvine, United States
| | - Marc A. Suchard
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, United States
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Universtiy of California, Los Angeles, United States
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9
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Ji X, Fisher AA, Su S, Thorne JL, Potter B, Lemey P, Baele G, Suchard MA. Scalable Bayesian Divergence Time Estimation With Ratio Transformations. Syst Biol 2023; 72:1136-1153. [PMID: 37458991 PMCID: PMC10636426 DOI: 10.1093/sysbio/syad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 06/13/2023] [Accepted: 06/30/2023] [Indexed: 11/08/2023] Open
Abstract
Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original $N-1$ internal node heights into a space of one height parameter and $N-2$ ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest.
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Affiliation(s)
- Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, 6823 St. Charles Avenue, New Orleans, LA 70118, USA
| | - Alexander A Fisher
- Department of Statistical Science, Duke University, 214 Old Chemistry, Durham, NC 27708, USA
| | - Shuo Su
- MOE International Joint Collaborative Research Laboratory for Animal Health & Food Safety, Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Nanjing Agricultural University, No. 1 Weigang, Xiaolingwei District, Nanjing, Jiangsu 210095, China
| | - Jeffrey L Thorne
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
- Department of Statistics, North Carolina State University, Raleigh, NC, USA
- Department of Biological Sciences, North Carolina State University, Ricks Hall, 1 Lampe Dr, Raleigh, NC 27607, USA
| | - Barney Potter
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Herestraat 49, 3000 Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Herestraat 49, 3000 Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Herestraat 49, 3000 Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA 90095, USA
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10
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McBride DS, Garushyants SK, Franks J, Magee AF, Overend SH, Huey D, Williams AM, Faith SA, Kandeil A, Trifkovic S, Miller L, Jeevan T, Patel A, Nolting JM, Tonkovich MJ, Genders JT, Montoney AJ, Kasnyik K, Linder TJ, Bevins SN, Lenoch JB, Chandler JC, DeLiberto TJ, Koonin EV, Suchard MA, Lemey P, Webby RJ, Nelson MI, Bowman AS. Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer. Nat Commun 2023; 14:5105. [PMID: 37640694 PMCID: PMC10462754 DOI: 10.1038/s41467-023-40706-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/07/2023] [Indexed: 08/31/2023] Open
Abstract
The zoonotic origin of the COVID-19 pandemic virus highlights the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected that SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, disseminating across hundreds of kilometers. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only three-times faster in white-tailed deer compared to the rate observed in humans but also driven by different mutational biases and selection pressures. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal models using white-tailed deer origin viruses. Still, SARS-CoV-2 has transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.
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Affiliation(s)
- Dillon S McBride
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Sofya K Garushyants
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - John Franks
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Andrew F Magee
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Steven H Overend
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Devra Huey
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Amanda M Williams
- Infectious Diseases Institute, The Ohio State University, Columbus, OH, USA
| | - Seth A Faith
- Infectious Diseases Institute, The Ohio State University, Columbus, OH, USA
| | - Ahmed Kandeil
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
- Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza, 12622, Egypt
| | - Sanja Trifkovic
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Lance Miller
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Trushar Jeevan
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Jacqueline M Nolting
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | | | - J Tyler Genders
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Columbus, OH, USA
| | - Andrew J Montoney
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Columbus, OH, USA
| | - Kevin Kasnyik
- Columbus and Franklin County Metro Parks, Westerville, OH, USA
| | - Timothy J Linder
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Sarah N Bevins
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Julianna B Lenoch
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Jeffrey C Chandler
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Wildlife Disease Diagnostic Laboratory, Fort Collins, CO, USA
| | - Thomas J DeLiberto
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Fort Collins, CO, USA
| | - Eugene V Koonin
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Richard J Webby
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Martha I Nelson
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
| | - Andrew S Bowman
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA.
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11
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Jun SH, Nasif H, Jennings-Shaffer C, Rich DH, Kooperberg A, Fourment M, Zhang C, Suchard MA, Matsen FA. A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm. Algorithms Mol Biol 2023; 18:10. [PMID: 37525243 PMCID: PMC10391877 DOI: 10.1186/s13015-023-00235-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
Bayesian phylogenetics is a computationally challenging inferential problem. Classical methods are based on random-walk Markov chain Monte Carlo (MCMC), where random proposals are made on the tree parameter and the continuous parameters simultaneously. Variational phylogenetics is a promising alternative to MCMC, in which one fits an approximating distribution to the unnormalized phylogenetic posterior. Previous work fit this variational approximation using stochastic gradient descent, which is the canonical way of fitting general variational approximations. However, phylogenetic trees are special structures, giving opportunities for efficient computation. In this paper we describe a new algorithm that directly generalizes the Felsenstein pruning algorithm (a.k.a. sum-product algorithm) to compute a composite-like likelihood by marginalizing out ancestral states and subtrees simultaneously. We show the utility of this algorithm by rapidly making point estimates for branch lengths of a multi-tree phylogenetic model. These estimates accord with a long MCMC run and with estimates obtained using a variational method, but are much faster to obtain. Thus, although generalized pruning does not lead to a variational algorithm as such, we believe that it will form a useful starting point for variational inference.
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Affiliation(s)
- Seong-Hwan Jun
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, USA
| | - Hassan Nasif
- Department of Statistics, University of Washington, Seattle, USA
| | - Chris Jennings-Shaffer
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - David H Rich
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Anna Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW Australia
| | - Cheng Zhang
- School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, USA
- Department of Computational Medicine, University of California, Los Angeles, USA
- Department of Biostatistics, University of California, Los Angeles, USA
| | - Frederick A Matsen
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington USA
- Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Mail stop: S2-140, Seattle, WA 98109-1024 USA
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12
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Tsui JLH, McCrone JT, Lambert B, Bajaj S, Inward RP, Bosetti P, Tegally H, Hill V, Pena RE, Zarebski AE, Peacock TP, Liu L, Wu N, Davis M, Bogoch II, Khan K, Kall M, Abdul Aziz NIB, Colquhoun R, O’Toole Á, Jackson B, Dasgupta A, Wilkinson E, de Oliveira T, Connor TR, Loman NJ, Colizza V, Fraser C, Volz E, Ji X, Gutierrez B, Chand M, Dellicour S, Cauchemez S, Raghwani J, Suchard MA, Lemey P, Rambaut A, Pybus OG, Kraemer MU. Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1. Science 2023; 381:336-343. [PMID: 37471538 PMCID: PMC10866301 DOI: 10.1126/science.adg6605] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country's human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.
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Affiliation(s)
| | - John T. McCrone
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Helix, San Mateo, USA
| | - Ben Lambert
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Paolo Bosetti
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Verity Hill
- Helix, San Mateo, USA
- Yale University, New Haven, USA
| | | | | | - Thomas P. Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | - Neo Wu
- Google Research, Mountain View, USA
| | | | - Isaac I. Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | | | | | | | | | | | - Eduan Wilkinson
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Thomas R. Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J. Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA
| | | | | | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Simon Cauchemez
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Jayna Raghwani
- Department of Biology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Marc A. Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | - Oliver G. Pybus
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
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13
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Pekar JE, Lytras S, Ghafari M, Magee AF, Parker E, Havens JL, Katzourakis A, Vasylyeva TI, Suchard MA, Hughes AC, Hughes J, Robertson DL, Dellicour S, Worobey M, Wertheim JO, Lemey P. The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548617. [PMID: 37502985 PMCID: PMC10369958 DOI: 10.1101/2023.07.12.548617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 has led to increased sampling of related sarbecoviruses circulating primarily in horseshoe bats. These viruses undergo frequent recombination and exhibit spatial structuring across Asia. Employing recombination-aware phylogenetic inference on bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed just ~1-3 years prior to their emergence in humans. Phylogeographic analyses examining the movement of related sarbecoviruses demonstrate that they traveled at similar rates to their horseshoe bat hosts and have been circulating for thousands of years in Asia. The closest-inferred bat virus ancestor of SARS-CoV likely circulated in western China, and that of SARS-CoV-2 likely circulated in a region comprising southwest China and northern Laos, both a substantial distance from where they emerged. This distance and recency indicate that the direct ancestors of SARS-CoV and SARS-CoV-2 could not have reached their respective sites of emergence via the bat reservoir alone. Our recombination-aware dating and phylogeographic analyses reveal a more accurate inference of evolutionary history than performing only whole-genome or single gene analyses. These results can guide future sampling efforts and demonstrate that viral genomic fragments extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.
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Affiliation(s)
- Jonathan E Pekar
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA
- These authors contributed equally
| | - Spyros Lytras
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
- These authors contributed equally
| | - Mahan Ghafari
- Department of Biology, University of Oxford, Oxford, UK
| | - Andrew F Magee
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Edyth Parker
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jennifer L Havens
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Tetyana I Vasylyeva
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Alice C Hughes
- School of Biological Sciences, University of Hong Kong, Hong Kong
- China Biodiversity Green Development Foundation, Beijing, China
| | - Joseph Hughes
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - David L Robertson
- Medical Research Council-University of Glasgow Centre for Virus Research, Glasgow, UK
- These authors jointly supervised the work
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, 1050, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
- These authors jointly supervised the work
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
- These authors jointly supervised the work
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- These authors jointly supervised the work
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
- These authors jointly supervised the work
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14
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Fourment M, Swanepoel CJ, Galloway JG, Ji X, Gangavarapu K, Suchard MA, Matsen IV FA. Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation. Genome Biol Evol 2023; 15:evad099. [PMID: 37265233 PMCID: PMC10282121 DOI: 10.1093/gbe/evad099] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023] Open
Abstract
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.
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Affiliation(s)
- Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia
| | - Christiaan J Swanepoel
- Centre for Computational Evolution, The University of Auckland, Auckland, New Zealand
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Jared G Galloway
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, Louisiana, USA
| | - Karthik Gangavarapu
- Department of Human Genetics, University of California, Los Angeles, California, USA
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, University of California, Los Angeles, California, USA
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Frederick A Matsen IV
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Statistics, University of Washington, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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15
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McBride D, Garushyants S, Franks J, Magee A, Overend S, Huey D, Williams A, Faith S, Kandeil A, Trifkovic S, Miller L, Jeevan T, Patel A, Nolting J, Tonkovich M, Genders JT, Montoney A, Kasnyik K, Linder T, Bevins S, Lenoch J, Chandler J, DeLiberto T, Koonin E, Suchard M, Lemey P, Webby R, Nelson M, Bowman A. Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer. RESEARCH SQUARE 2023:rs.3.rs-2574993. [PMID: 36824718 PMCID: PMC9949239 DOI: 10.21203/rs.3.rs-2574993/v1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
While SARS-CoV-2 has sporadically infected a wide range of animal species worldwide1, the virus has been repeatedly and frequently detected in white-tailed deer in North America2â€"7. The zoonotic origins of this pandemic virus highlight the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, which disseminated across hundreds of kilometers. We discovered that alpha and delta variants evolved in white-tailed deer at three-times the rate observed in humans. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only faster in white-tailed deer but driven by different mutational biases and selection pressures. White-tailed deer are not just short-term recipients of human viral diversity but serve as reservoirs for alpha and other variants to evolve in new directions after going extinct in humans. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal model experiments using viruses isolated from white-tailed deer. Still, SARS-CoV-2 viruses have transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.
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Affiliation(s)
- Dillon McBride
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Sofya Garushyants
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - John Franks
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Andrew Magee
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Steven Overend
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Devra Huey
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Amanda Williams
- Infectious Disease Institute, The Ohio State University, Columbus, OH, USA
| | - Seth Faith
- Infectious Disease Institute, The Ohio State University, Columbus, OH, USA
| | - Ahmed Kandeil
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Sanja Trifkovic
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Lance Miller
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Trushar Jeevan
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Jacqueline Nolting
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
| | - Michael Tonkovich
- Ohio Department of Natural Resources, Division of Wildlife, Athens, OH, USA
| | - J Tyler Genders
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Columbus, OH, USA
| | | | | | - Timothy Linder
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Sarah Bevins
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Julianna Lenoch
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, CO, USA
| | - Jeffrey Chandler
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Wildlife Disease Diagnostic Laboratory, Fort Collins, CO, USA
| | - Thomas DeLiberto
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Fort Collins, CO, USA
| | - Eugene Koonin
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Marc Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Richard Webby
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Martha Nelson
- Division of Intramural Research, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Andrew Bowman
- Department of Veterinary Preventive Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA
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16
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Fisher AA, Hassler GW, Ji X, Baele G, Suchard MA, Lemey P. Scalable Bayesian phylogenetics. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210242. [PMID: 35989603 PMCID: PMC9393558 DOI: 10.1098/rstb.2021.0242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Affiliation(s)
| | - Gabriel W. Hassler
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
| | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
| | - Marc A. Suchard
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA,Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA,Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium
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17
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Hassler GW, Magee A, Zhang Z, Baele G, Lemey P, Ji X, Fourment M, Suchard MA. Data integration in Bayesian phylogenetics. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2022; 10:353-377. [PMID: 38774036 PMCID: PMC11108065 DOI: 10.1146/annurev-statistics-033021-112532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.
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Affiliation(s)
- Gabriel W Hassler
- Department of Computational Medicine, University of California, Los Angeles, USA, 90095
| | - Andrew Magee
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
| | - Zhenyu Zhang
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium, 3000
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium, 3000
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA, 70118
| | - Mathieu Fourment
- Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo NSW, Australia, 2007
| | - Marc A Suchard
- Department of Computational Medicine, University of California, Los Angeles, USA, 90095
- Department of Biostatistics, University of California, Los Angeles, USA, 90095
- Department of Human Genetics, University of California, Los Angeles, USA, 90095
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18
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Abstract
The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.
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Affiliation(s)
- Caleb Ki
- Department of Statistics, University of Michigan
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19
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Koskela J. Zig-zag sampling for discrete structures and non-reversible phylogenetic MCMC. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2032722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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Bastide P, Ho LST, Baele G, Lemey P, Suchard MA. Efficient Bayesian inference of general Gaussian models on large phylogenetic trees. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Lam Si Tung Ho
- Department of Mathematics and Statistics, Dalhousie University
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven
| | - Marc A. Suchard
- Departments of Biostatistics, Biomathematics, and Human Genetics, University of California, Los Angeles
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21
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Abstract
In 1981, the Journal of Molecular Evolution (JME) published an article entitled "Evolutionary trees from DNA sequences: A maximum likelihood approach" by Joseph (Joe) Felsenstein (J Mol Evol 17:368-376, 1981). This groundbreaking work laid the foundation for the emerging field of statistical phylogenetics, providing a tractable way of finding maximum likelihood (ML) estimates of evolutionary trees from DNA sequence data. This paper is the second most cited (more than 9000 citations) in JME after Kimura's (J Mol Evol 16:111-120, 1980) seminal paper on a model of nucleotide substitution (with nearly 20,000 citations). On the occasion of the 50th anniversary of JME, we elaborate on the significance of Felsenstein's ML approach to estimating phylogenetic trees.
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Affiliation(s)
- David Posada
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain.
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310, Vigo, Spain.
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain.
| | - Keith A Crandall
- Computational Biology Institute and Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
- Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
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22
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Baele G, Gill MS, Lemey P, Suchard MA. Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework. Wellcome Open Res 2020; 5:53. [PMID: 32923688 PMCID: PMC7463299 DOI: 10.12688/wellcomeopenres.15770.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2020] [Indexed: 11/20/2022] Open
Abstract
Nonparametric coalescent-based models are often employed to infer past population dynamics over time. Several of these models, such as the skyride and skygrid models, are equipped with a block-updating Markov chain Monte Carlo sampling scheme to efficiently estimate model parameters. The advent of powerful computational hardware along with the use of high-performance libraries for statistical phylogenetics has, however, made the development of alternative estimation methods feasible. We here present the implementation and performance assessment of a Hamiltonian Monte Carlo gradient-based sampler to infer the parameters of the skygrid model. The skygrid is a popular and flexible coalescent-based model for estimating population dynamics over time and is available in BEAST 1.10.5, a widely-used software package for Bayesian pylogenetic and phylodynamic analysis. Taking into account the increased computational cost of gradient evaluation, we report substantial increases in effective sample size per time unit compared to the established block-updating sampler. We expect gradient-based samplers to assume an increasingly important role for different classes of parameters typically estimated in Bayesian phylogenetic and phylodynamic analyses.
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Affiliation(s)
- Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Marc A Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, 695 Charles E. Young Drive, Los Angeles, California, 90095-1766, USA
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