Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Abstract

Whole genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analysing 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 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 characterise 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 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 at identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared to 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 where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.

A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status

“…Isolate SRR5153901 had one low frequency variant in the rpoB RRDR that was not a recognized DR variant. There are several potential sources for this disagreement, including differences in diversity between WGVA samples and pDST samples due to mixed infection in the original samples [28] and localized failure of individual DSTs.…”

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

“…While our focus is on M. tuberculosis , a bacterium that is considered to be slow-evolving 48,55,60 , the issues we identify here generalize to other pathogens 61 . Our results suggest that pathogen genomic epidemiology, for M. tuberculosis and other species, will benefit from genomic resources similar to those that exist for human genomes.…”

Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues

“…Other approaches have meanwhile been developed to improve the identification of epidemiological links and outbreak reconstruction beyond SNP-based clustering. These either use transmission event thresholds 121 and/or often combine genomic and epidemiological data 511 to identify the most probable transmission trees for infectious diseases 122,123 . Of particular 512 importance when reconstructing Mtbc outbreaks is that phylogeny and transmission events do 513 not necessarily coincide as a results of genetic diversification during latency and long generation times 124 ; it is thus necessary to model the within-host genetic dynamics 125-127 .…”