hogwash: Three Methods for Genome-Wide Association Studies in Bacteria

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. DATA SUMMARYO_LIhogwash is available from GitHub under the MIT license (https://github.com/katiesaund/hogwash) and can be installed using the R commands install.packages("devtools") devtools::install_github("katiesaund/hogwash") C_LIO_LIThe simulated data used in this manuscript and the code to generate it are available from GitHub (https://github.com/katiesaund/simulate_data_for_convergence_based_bGWAS) C_LI IMPACT STATEMENTWe introduce hogwash, an R package with three methods for bacterial genome-wide association studies. There are two methods for handling binary phenotypes, including an implementation of PhyC(1), as well as one method for handling continuous phenotypes. We formulate novel indices quantifying the relationship between phenotype convergence and genotype convergence on a phylogenetic tree. These indices shape an intuitive understanding for the ability of hogwash to detect significant intersections of phenotype convergence and genotype convergence and how to interpret hogwash outputs.