Identifying lineage effects when controlling for population structure improves power in bacterial association studies

TL;DR

This paper introduces a new bacterial GWAS method that captures lineage effects to improve detection of resistance genes, overcoming limitations of existing strain correction techniques.

Contribution

The authors propose a novel approach that detects lineage-level associations in bacterial GWAS, enhancing power to identify resistance mechanisms when locus-specific mapping is difficult.

Findings

Successfully identified known resistance genes across four bacterial species.

Revealed a novel association between nmpC and cefazolin resistance in E. coli.

Demonstrated increased power in detecting genetic factors compared to traditional methods.

Abstract

Bacteria pose unique challenges for genome-wide association studies (GWAS) because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome. While methods developed for human studies can correct for strain structure, this risks considerable loss- of-power because genetic differences between strains often contribute substantial phenotypic variability. 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 3144 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.