Abstract
BackgroundWhole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods.MethodsWe comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses.ResultsTwenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data.ConclusionsAs sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0566-x) contains supplementary material, which is available to authorized users.
Highlights
Whole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches
Papers were double-screened by H-AH and JRW and included if they analysed WGS data to investigate the transmission of Mycobacterium tuberculosis (M.tb), according to any of the four topics prioritised for this review (Fig. 1)
An alternative approach [17] determined the variation between improbable transmission pairs first and, as no pair had less than 2 single nucleotide polymorphism (SNP) difference, used 0–1 SNPs between sequences to define a cluster
Summary
Whole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. The ability of whole genome sequencing (WGS) [1] to discriminate between pathogen strains that are indistinguishable using other typing methods has greatly advanced the field of molecular epidemiology. The limited diversity and complicated natural history of TB infection needs special consideration, many of the methods discussed in this review are employed for studying transmission of other pathogens (e.g. SARS coronavirus [8], methicillin-resistant Staphylococcus aureus [9] and Clostridium difficile [10]) and many of the issues raised will apply to these pathogens. This review describes the methods used to analyse WGS data, their limitations and implications for clinical application
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