Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions
Top Cited Papers
- 25 January 2019
- journal article
- research article
- Published by Oxford University Press (OUP) in Molecular Biology and Evolution
- Vol. 36 (3), 587-603
- https://doi.org/10.1093/molbev/msy242
Abstract
Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define “transmission clusters,” sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.Keywords
Funding Information
- The Engineering and Physical Sciences Research Council (EP/K026003/1)
- EPSRC (EP/N014529/1, U54GM088558)
- National Institute of General Medical Sciences
- National Institute of General Medical Sciences
- National Institutes of Health
This publication has 49 references indexed in Scilit:
- Evolutionary Dynamics of Vibrio cholerae O1 following a Single-Source Introduction to HaitimBio, 2013
- Finding Evidence for Local Transmission of Contagious Disease in Molecular Epidemiological DatasetsPLOS ONE, 2013
- Inferring patient to patient transmission of Mycobacterium tuberculosisfrom whole genome sequencing dataBMC Infectious Diseases, 2013
- Whole Genome Sequencing versus Traditional Genotyping for Investigation of a Mycobacterium tuberculosis Outbreak: A Longitudinal Molecular Epidemiological StudyPLoS Medicine, 2013
- Using statistical methods and genotyping to detect tuberculosis outbreaksInternational Journal of Health Geographics, 2013
- Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational studyThe Lancet Infectious Diseases, 2012
- Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infectionNature Genetics, 2011
- phangorn: phylogenetic analysis in RBioinformatics, 2010
- Resolving the impact of waiting time distributions on the persistence of measlesJournal of The Royal Society Interface, 2009
- Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteriaBiology Direct, 2009