ArticleMicrobial genomics2020
prewas: data pre-processing for more informative bacterial GWAS.
Article in Microbial genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 28 citations in OpenAlex.
- Genomic analysis reveals two dominant strains ofApplied and environmental microbiology · 2025Article
- Global-scale GWAS associates a subset of SNPs with animal-adapted variants in M. tuberculosis complex.BMC medical genomics · 2023Article
- The genetic network underlying the evolution of pathogenicity in avianFrontiers in veterinary science · 2023Article
- Genome-wide association study identifies a gene responsible for temperature-dependent rice germination.Nature communications · 2022Article
- Genome-Wide Study of Drug ResistantMicroorganisms · 2022Article
- Article
- Hogwash: three methods for genome-wide association studies in bacteria.Microbial genomics · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS, which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. prewas is available for download from GitHub.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.