Abstract
Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.
Highlights
An R package with three methods for bacterial genome-wide association studies
We have developed two algorithms for convergence based Bacterial genome-wide association studies (bGWAS) that are well suited for phenotypes modeled by white noise
Hogwash, is straightforward to install in R, accepts easy-to-format data inputs, and provides publication ready plots of the GWAS results
Summary
Bacterial Genome-Wide Association StudiesBacterial genome-wide association studies (bGWAS) infer statistical associations between genotypes and phenotypes. Seminal bGWAS papers identified novel variants associated with antibiotic resistance in M. tuberculosis and host specificity in Campylobacter[1,2]. There have been numerous applications of bGWAS that have further highlighted the potential of this approach to identify genetic pathways underlying phenotypic variation and provide insights into the evolution of phenotypes of interest. Association studies can use various genetic data types including single nucleotide polymorphisms (SNPs), k-mers, copy number variants, accessory genes, insertions, and deletions. Differences between human and bacterial GWAS have been reviewed extensively by Power et al[5]. Clonality and horizontal gene transfer complicate the application of human GWAS methodology to bacteria. BGWAS approaches can leverage unique features of bacterial evolution, including frequent phenotypic convergence and genotypic convergence, to identify phenotype-genotype correlations
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