Emerging Concepts of Data Integration in Pathogen Phylodynamics

被引:62
作者
Baele, Guy [1 ]
Suchard, Marc A. [2 ,3 ,4 ]
Rambaut, Andrew [5 ,6 ]
Lemey, Philippe [1 ]
机构
[1] Univ Leuven, 1Department Microbiol & Immunol, Rega Inst, Leuven, Belgium
[2] Univ Calif Los Angeles, Dept Biomathemat, Los Angeles, CA 90095 USA
[3] Univ Calif Los Angeles, Dept Human Genet, David Geffen Sch Med, Los Angeles, CA 90095 USA
[4] Univ Calif Los Angeles, Dept Biostatist, Sch Publ Hlth, Los Angeles, CA 90095 USA
[5] Univ Edinburgh, Inst Evolutionary Biol, Edinburgh EH9 3FL, Midlothian, Scotland
[6] Univ Edinburgh, Ctr Immun, Infect & Evolut, Edinburgh EH9 3FL, Midlothian, Scotland
基金
美国国家科学基金会; 欧洲研究理事会; 美国国家卫生研究院;
关键词
Bayesian inference; birth-death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics; ANCESTRAL CHARACTER STATES; EFFECTIVE POPULATION SIZES; EVOLUTIONARY ANALYSIS; PHYLOGENETIC ANALYSIS; MOLECULAR EVOLUTION; BAYESIAN-INFERENCE; DNA-SEQUENCES; WITHIN-HOST; VIRUS; LIKELIHOOD;
D O I
10.1093/sysbio/syw054
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees.
引用
收藏
页码:E47 / E65
页数:19
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