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.

中文翻译：

---

Hogwash：细菌全基因组关联研究的三种方法

细菌全基因组关联研究 (bGWAS) 捕获基因组变异和表型变异之间的关联。基于收敛的 bGWAS 方法识别在表型变异存在的情况下在系统发育树上独立发生多次的基因组突变，这比偶然预期的要频繁。这项工作介绍了 hogwash，这是一个开源 R 包，它为基于收敛的 bGWAS 实现了三种算法。Hogwash 还包含两种负担测试方法来执行基因或通路分析，以提高相关但弱外显基因型的能力并增加收敛检测。为了确定最佳用例，我们将 hogwash 应用于使用各种系统发育信号和收敛分布模拟的数据。这些模拟数据是公开可用的，并包含有关每个表型和基因型的收敛和系统发育信号的相关元数据。Hogwash 可从 GitHub 下载。