ArticleBMC bioinformatics2025
GrafGen: distance-based inference of population ancestry for Helicobacter pylori genomes.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Genomic landscape and fine-scale population structure of Helicobacter pylori across China.Biomarker research · 2026Article
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6 authors.
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Abstract
backgroundHelicobacter pylori is a highly diverse gastric bacterium whose genomic variation both reflects human migration and complicates genome-wide association studies (GWAS). Its 1.67 Mb genome contains ~ 143,000 biallelic SNPs with minor allele frequency > 1%, making population stratification a major confounder. Existing model- and distance-based methods for bacterial ancestry classification often yield inconsistent results depending on dataset composition. A robust and generalizable framework is needed to improve downstream analyses.
resultsWe developed GrafGen, an open-source R package adapted from the human ancestry tool GrafPop, for the classification of H. pylori and prophage populations. Using reference data from the H. pylori Genome Project (1,011 genomes from 50 countries), GrafGen identified nine distinct bacterial populations and four prophage groups by genetic distance clustering. Validation with 255 GenBank sequences showed consistent mapping to GrafGen-defined populations. Classifications based on subsets of 14,300 and 1,430 SNPs achieved > 97% and > 90% concordance, respectively, with those using the full 143,000 SNPs, demonstrating robustness to down-sampling. The package integrates visualization tools for geometric interpretation of ancestry structure and is distributed via Bioconductor (v1.4.0, nine-population reference) and GitHub (v2.0_beta, general framework for haploid species and prophages).
conclusionsGrafGen provides a reliable approach for classifying H. pylori ancestry and correcting for bacterial population stratification in GWAS. By enabling more accurate inference of genotype-phenotype associations, the method enhances studies of bacterial genetics and host-pathogen interactions. The underlying algorithm is extensible to other haploid organisms with adequate reference data, broadening its relevance beyond H. pylori.
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