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Nat Microbiol. 2016 Apr 4;1:16041. doi: 10.1038/nmicrobiol.2016.41.

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

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Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK.
Antimicrobial Resistance and Healthcare Associated Infections Reference Unit, Public Health England, London NW9 5EQ, UK.
Public Health England, West Midlands Public Health Laboratory, Heartlands Hospital, Birmingham B9 5SS, UK.
Centre for Tuberculosis, National Institute for Communicable Diseases, Johannesburg 2131 South Africa.
Department of Medical Microbiology, University of Pretoria, Pretoria, South Africa.
Department of Infectious Diseases and Microbiology, Royal Sussex County Hospital, Brighton BN2 5BE, UK.
The Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK.
Contributed equally


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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