Abstract
The inference of disease transmission networks from genetic sequence data is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from (a subset of) infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference cannot take within-host strain diversity into account, which affects their accuracy, and, moreover, are highly computationally intensive and unscalable.
In this work, we introduce a new phylogenetic approach, TNet, for inferring transmission networks that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity using a similar computational framework. TNet is freely available open-source from https://compbio.engr.uconn.edu/software/TNet/.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002). https://doi.org/10.1103/RevModPhys.74.47
Clutter, D., et al.: Trends in the molecular epidemiology and genetic mechanisms of transmitted human immunodeficiency virus type 1 drug resistance in a large US clinic population. Clin. Infect. Dis. 68(2), 213–221 (2018). https://doi.org/10.1093/cid/ciy453
De Maio, N., Worby, C.J., Wilson, D.J., Stoesser, N.: Bayesian reconstruction of transmission within outbreaks using genomic variants. PLoS Comp. Biol. 14(4), 1–23 (2018). https://doi.org/10.1371/journal.pcbi.1006117
De Maio, N., Wu, C.H., Wilson, D.J.: Scotti: Efficient reconstruction of transmission within outbreaks with the structured coalescent. PLoS Comp. Biol. 12(9), 1–23 (2016). https://doi.org/10.1371/journal.pcbi.1005130
Didelot, X., Fraser, C., Gardy, J., Colijn, C., Malik, H.: Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol. Biol. Evol. 34(4), 997–1007 (2017). https://doi.org/10.1093/molbev/msw275
Domingo, E., Holland, J.: RNA virus mutations and fitness for survival. Annu. Rev. Microbiol. 51, 151–178 (1997)
Drake, J.W., Holland, J.J.: Mutation rates among RNA viruses. Proc. Natl. Acad. Sci. USA 96(24), 13910–13913 (1999)
Fitch, W.: Towards defining the course of evolution: minimum change for a specified tree topology. Syst. Zool. 20, 406–416 (1971)
Glebova, O., Knyazev, S., Melnyk, A., Artyomenko, A., Khudyakov, Y., Zelikovsky, A., Skums, P.: Inference of genetic relatedness between viral quasispecies from sequencing data. BMC Genom. 18(suppl. 10), 918 (2017)
Grulich, A., et al.: A10 Using the molecular epidemiology of HIV transmission in New South Wales to inform public health response: Assessing the representativeness of linked phylogenetic data. Virus Evol. 4(suppl. 1), April 2018. https://doi.org/10.1093/ve/vey010.009
Hall, M., Woolhouse, M., Rambaut, A.: Epidemic reconstruction in a phylogenetics framework: transmission trees as partitions of the node set. PLoS Comp. Biol. 11(12), e1004613 (2015). https://doi.org/10.1371/journal.pcbi.1004613
Kermack, W.O., McKendrick, A.G., Walker, G.T.: A contribution to the mathematical theory of epidemics. Proc. Roy. Soci. Lond. Ser. A 115(772), 700–721 (1927). https://doi.org/10.1098/rspa.1927.0118
Klinkenberg, D., Backer, J.A., Didelot, X., Colijn, C., Wallinga, J.: Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. PLoS Comp. Biol. 13, 1–32 (2017). https://doi.org/10.1371/journal.pcbi.1005495
Kosakovsky Pond, S.L., Weaver, S., Leigh Brown, A.J., Wertheim, J.O.: HIV-TRACE (TRAnsmission Cluster Engine): a tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens. Mol. Biol. Evol. 35(7), 1812–1819 (2018). https://doi.org/10.1093/molbev/msy016
Moshiri, N., Wertheim, J.O., Ragonnet-Cronin, M., Mirarab, S.: FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences. Bioinformatics 35(11), 1852–1861 (2019). https://doi.org/10.1093/bioinformatics/bty921
Romero-Severson, E.O., Bulla, I., Leitner, T.: Phylogenetically resolving epidemiologic linkage. Proc. Natl. Acad. Sci. 113(10), 2690–2695 (2016). https://doi.org/10.1073/pnas.1522930113
Sankoff, D.: Minimal mutation trees of sequences. SIAM J. Appl. Math. 28(1), 35–42 (1975). http://www.jstor.org/stable/2100459
Sashittal, P., El-Kebir, M.: SharpTNI: counting and sampling parsimonious transmission networks under a weak bottleneck. bioRxiv (2019). https://doi.org/10.1101/842237
Skums, P., et al.: QUENTIN: reconstruction of disease transmissions from viral quasi species genomic data. Bioinformatics 34(1), 163–170 (2018). https://doi.org/10.1093/bioinformatics/btx402
Sledzieski, S., Zhang, C., Mandoiu, I., Bansal, M.S.: TreeFix-TP: phylogenetic error-correction for infectious disease transmission network inference. bioRxiv (2019). https://doi.org/10.1101/813931
Stamatakis, A.: RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30(9), 1312–1313 (2014). https://doi.org/10.1093/bioinformatics/btu033
Steinhauer, D., Holland, J.: Rapid evolution of RNA viruses. Annu. Rev. Microbiol. 41, 409–433 (1987)
Wymant, C., et al.: PHYLOSCANNER: inferring transmission from within- and between-host pathogen genetic diversity. Mol. Biol. Evol. 35(3), 719–733 (2017). https://doi.org/10.1093/molbev/msx304
Acknowledgements
The authors wish to thank Dr. Pavel Skums (Georgia State University) and the Centers for Disease Control for sharing their HCV outbreak data. We also thank Samuel Sledzieski for creating and sharing the simulated transmission network datasets used in this work.
Funding
This work was supported in part by NSF award CCF 1618347 to IM and MSB.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dhar, S., Zhang, C., Mandoiu, I., Bansal, M.S. (2020). TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-57821-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57820-6
Online ISBN: 978-3-030-57821-3
eBook Packages: Computer ScienceComputer Science (R0)