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Description
The WHO set a target for ‘End TB Strategy’ to include a 90% reduction in TB deaths and an 80% reduction in TB incidence by 2030. One short-term goal they set for 2018 was to identify high-confidence drug resistance conferring mutations in the M. tuberculosis genome. This set of mutations could improve genomic-based diagnostics, which are far more rapid than standard methods. Phenotypic TB resistance determination methods require weeks of culturing before drug susceptibility testing (DST) is performed. My thesis focuses on two projects aimed to help advance genomics-based diagnostics: First, develop a genome wide association study tool with user-defined filtering criteria. Second, identify regions in the M. tuberculosis Genome with consistently low sequencing coverage (“blind spots”) with commonly used sequencing technologies. Genome wide association study (GWAS) is a common method for identifying genomic markers for various traits across diverse organisms. Here, I created a GWAS tool using python. Known resistance-conferring mutations were among top hits for rifampicin, isoniazid, fluoroquinolones, and injectables, validating the tool. The sensitivity and specificity filtering along with the different types of associations, were able to uncover stronger hits than mutation-based or unfiltered gene-based results, showing the usefulness of this GWAS tool in complement over existing tools for some mutation patterns associated with resistance. Various sequencing platforms are used to sequence M. tuberculosis genomes, yet the limitations and biases of each are not thoroughly explored in the M. tuberculosis literature. The second arm of this thesis aims to fill this gap by generating a list of “blind spots” for three common sequencing platforms: Illumina, Ion Torrent and Pacific Biosciences (PacBio). These blind spots can be used by anyone working with M. tuberculosis genomic data to inform which platform they should use depending on the regions of interest and which bases they might consider excluding given their chosen platform. Together, these two projects will better inform researchers of the quality of their data for particular sites of interest, and complement existing GWAS tools, allowing candidate drug-resistance conferring mutations and genes to be identified more readily, and helping inform genomics-based resistance determination methods.
