ArticlemBio2020
Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions.
Article in mBio, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 140 citations in OpenAlex.
- Pooled it
- Skin Infection Pathogenicity Associated with a Canine Microbiome Resident: Polygenic Architecture of Virulence Factors inAntibiotics (Basel, Switzerland) · 2026Article
- Molecular determinants associated with resistance to imipenem and imipenem-relebactam in clinical Pseudomonas aeruginosa isolates.BMC genomics · 2026Article
- Genotype-phenotype modeling of light ecotypes inbioRxiv : the preprint server for biology · 2026Article
- Capsular specificity in temperate phages of Klebsiella pneumoniae is driven by diverse receptor-binding enzymes.PLoS biology · 2026Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Large-scale testing of antimicrobial lethality at single-cell resolution predicts mycobacterial infection outcomes.Nature microbiology · 2026Article
- Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes forMicrobial genomics · 2026Article
- Article
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- Suppression of gut colonization by multidrug-resistant Escherichia coli clinical isolates through cooperative niche exclusion.Nature communications · 2025Article
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
- Comparison of pharyngeal and invasive isolates ofMicrobiology spectrum · 2025Article
- Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.Scientific reports · 2025Article
- aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.Genome biology · 2025Article
- Article
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
- TolRad, a model for predicting radiation tolerance using Pfam annotations, identifies novel radiosensitive bacterial species from reference genomes and MAGs.Microbiology spectrum · 2024Article
- Where the Patterns Are: Repetition-Aware Compression for Colored de Bruijn GraphsJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
Abstract
Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially.
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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.