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Updated: Dec 23, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

prewas: data pre-processing for more informative bacterial GWAS.

Katie Saund¹, Zena Lapp², Stephanie N Thiede¹

¹Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.

Microbial Genomics

|April 21, 2020

Summary

This summary is machine-generated.

Standardizing bacterial variant pre-processing is crucial for accurate genome-wide association studies (bGWAS). The prewas R package addresses multiallelic sites, overlapping genes, and ancestral alleles to improve bGWAS reproducibility and interpretability.

Keywords:

GWAS data pre-processing multiallelic loci overlapping genes reference allele software

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Area of Science:

Genomics
Bioinformatics
Population Genetics

Background:

Standardized variant identification pipelines exist, but pre-processing steps for bacterial genome-wide association studies (bGWAS) are often overlooked.
Key pre-processing nuances impacting bGWAS include handling multiallelic sites, overlapping genes, and ancestral allele referencing.

Purpose of the Study:

To demonstrate the impact of variant pre-processing on bGWAS.
To introduce 'prewas', an R package designed to standardize variant pre-processing for bGWAS.

Main Methods:

The study evaluated the importance of variant pre-processing steps using diverse bacterial genomic datasets.
The 'prewas' R package was developed to standardize the handling of multiallelic sites, overlapping genes, and reference alleles.

Main Results:

Pre-processing variants is critical for accurate genetic association identification in bacteria.
'prewas' implements multi-line representation for multiallelic sites and overlapping genes, preventing minor allele masking.
The package outputs binary SNP and optional gene matrices for SNP-based and gene-based bGWAS, respectively.

Conclusions:

The 'prewas' package enhances the reproducibility and interpretability of bGWAS results.
Utilizing 'prewas' allows for maximal information extraction from bGWAS, improving analytical power and evolutionary insights.