A phylogenetic method to perform genome-wide association studies in microbes
Creators: name: Collins, Caitlin
Publisher: name: Imperial College London
Year: 2019

description: Genome-Wide Association Studies (GWAS) are designed to perform an unbiased search of genetic sequence data with the intent of identifying statistically significant associations with a phenotype or trait of interest. The application of GWAS methods to microbial organisms promises to improve the way we understand, manage, and treat infectious diseases. Yet, while microbial pathogens continue to undermine human health, wealth, and longevity, microbial GWAS methods remain unable to fully capitalise on the growing wealth of bacterial and viral genetic sequence data. Clonal population structure and homologous recombination in microbial organisms make it difficult for existing GWAS methods to achieve both the precision needed to reject false positive findings and the statistical power required to detect genuine associations between microbial genotypic and phenotypic variants. In this thesis, we investigate potential solutions to the most substantial methodological challenges in microbial GWAS, and we introduce a new phylogenetic GWAS approach that has been specifically designed for use in bacterial samples. In presenting our approach, we describe the features that render it robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Our approach is applicable to organisms ranging from purely clonal to frequently recombining, to sequence data from both the core and accessory genome, and to binary, categorical, and continuous phenotypes. We also describe the efforts taken to make our method efficient, scalable, and accessible in its implementation within the open-source R package we have created, called treeWAS. Next, we apply our GWAS method to simulated datasets. We develop multiple frameworks for simulating genotypic and phenotypic data with control over relevant parameters. We then present the results of our simulation study, and we use thorough performance testing to demonstrate the power and specificity of our approach, as compared to the performance of alternative cluster-based and dimension-reduction methods. Our approach is then applied to three empirical datasets, from Neisseria gonorrhoeae and Neisseria meningitidis, where we identify core SNPs associated with binary drug resistance and continuous antibiotic minimum inhibitory concentration phenotypes, as well as both core SNP and accessory genome associations with invasive and commensal phenotypes. These applications illustrate the versatility and potential of our method, demonstrating in each case that our approach is capable of confirming known resistance- or virulence-associated loci and discovering novel associations. Our thesis concludes with a review of the previous chapters and an evaluation of the strengths and limitations displayed by the current implementation of our phylogenetic approach to association testing. We discuss key areas for further development, and we propose potential solutions to advance the development of microbial GWAS in future work.