Understanding the transmission of tuberculosis using Mycobacterium tuberculosis sequence data

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Tuberculosis is an infectious disease that causes a high public health burden. The World Health Organisation estimates that there are nine million new cases and nearly two million deaths each year. Establishing who transmits to whom and where is fundamental to disease control. By comparing the genetic profiles of tuberculosis-causing bacteria within large population-based studies we can identify likely transmissions based on the similarity of the strains. Our work will focus initially on a district of Malawi where tuberculosis disease prevalence has been high, and data and tuberculosis samples from nearly 2500 patients have been collected over twenty years. The inferred genetic profiles will use a higher proportion of the genome than used previously, allowing more accurate reconstructions of transmission chains. Factors influencing transmissibility will then be assessed directly by looking at the patterns of data within each transmission chain. These factors may be genetic variations within the bacteria themselves, or differences in the human hosts, including age, sex, HIV infection and treatment, and contact patterns. We will attempt to confirm any insights into these factors using data collected and generated from other populations. Ultimately, an improved understanding of the genetic and other processes underlying transmissibility could lead to the development of improved control measures. These could include novel drug or vaccine targets, or identification of geographical or socially determined hotspots of transmission.