ArticleNature microbiology2016
Identifying lineage effects when controlling for population structure improves power in bacterial association studies.
Article in Nature microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 2 of them syntheses that pooled it.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
176 citing papers in PubMed, 2 syntheses or guidelines pooled it, 313 citations in OpenAlex.
- Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
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- A genome-wide association study to identify the genetic loci underlying carbapenem resistance in Acinetobacter baumannii.BMC genomics · 2026Article
- Lineage-aware comparison of extended-spectrumMicrobial genomics · 2026Article
- Convergent evolution of ST2 and ST164 mediates dissemination of the blaAnnals of clinical microbiology and antimicrobials · 2026Article
- GeneScanner: profiling genetic variation across bacterial populations.Microbial genomics · 2026Article
- A versatile multi-components mixed model for bacterial-Genome Wide association studies.Nature communications · 2026Article
- Article
- Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response.Nature communications · 2026Article
- Genomic and statistical models to characterizeMicrobial genomics · 2026Article
- BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies.BMC microbiology · 2026Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Genome-wide association study of Mycoplasma anserisalpingitidis strains for antibiotic susceptibility.Scientific reports · 2026Article
- Whole-Genome Resequencing Reveals Population Structure and Loci Associated with Growth and Meat-Quality Traits in Meigu Yanying Chickens.Animals : an open access journal from MDPI · 2026Article
- A Clinically Silent Resistance Phenotype That PromotesbioRxiv : the preprint server for biology · 2026Article
- Comprehensive genomics, systems, and structural assessment for novel target identification in penicillin-resistantFrontiers in bioinformatics · 2026Article
- Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.PLoS biology · 2025Article
- Machine learning and statistical inference in microbial population genomics.Genome biology · 2025Review
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
116 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors at 6 institutions in 3 countries.
Funding
Abstract
Bacteria pose unique challenges for genome-wide association studies because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome(1,2). Although methods developed for human studies can correct for strain structure(3,4), this risks considerable loss-of-power because genetic differences between strains often contribute substantial phenotypic variability(5). Here, we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3,144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence, our method pinpoints locus-specific effects where possible and boosts power by detecting lineage-level differences when fine-mapping is intractable.
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What OpenQuestion holds
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.