Literature DB >> 27572646

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

Sarah G Earle1, Chieh-Hsi Wu1, Jane Charlesworth1, Nicole Stoesser1, N Claire Gordon1, Timothy M Walker1, Chris C A Spencer2, Zamin Iqbal2, David A Clifton3, Katie L Hopkins4, Neil Woodford4, E Grace Smith5, Nazir Ismail6,7, Martin J Llewelyn8, Tim E Peto1, Derrick W Crook1, Gil McVean2,9, A Sarah Walker1, Daniel J Wilson1,2.   

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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Mesh:

Year:  2016        PMID: 27572646      PMCID: PMC5049680          DOI: 10.1038/nmicrobiol.2016.41

Source DB:  PubMed          Journal:  Nat Microbiol        ISSN: 2058-5276            Impact factor:   17.745


  55 in total

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Journal:  Annu Rev Microbiol       Date:  2001       Impact factor: 15.500

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4.  Principal components analysis corrects for stratification in genome-wide association studies.

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6.  Molecular evolution of the Escherichia coli chromosome. III. Clonal frames.

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Journal:  Nat Methods       Date:  2012-03-04       Impact factor: 28.547

8.  Advantages and pitfalls in the application of mixed-model association methods.

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Journal:  Nat Genet       Date:  2014-02       Impact factor: 38.330

9.  Predicting the virulence of MRSA from its genome sequence.

Authors:  Maisem Laabei; Mario Recker; Justine K Rudkin; Mona Aldeljawi; Zeynep Gulay; Tim J Sloan; Paul Williams; Jennifer L Endres; Kenneth W Bayles; Paul D Fey; Vijaya Kumar Yajjala; Todd Widhelm; Erica Hawkins; Katie Lewis; Sara Parfett; Lucy Scowen; Sharon J Peacock; Matthew Holden; Daniel Wilson; Timothy D Read; Jean van den Elsen; Nicholas K Priest; Edward J Feil; Laurence D Hurst; Elisabet Josefsson; Ruth C Massey
Journal:  Genome Res       Date:  2014-04-09       Impact factor: 9.043

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Authors:  Jessica Hedge; Daniel J Wilson
Journal:  mBio       Date:  2014-11-25       Impact factor: 7.867

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3.  Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes.

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Journal:  Gigascience       Date:  2020-10-17       Impact factor: 6.524

5.  Bacterial GWAS: not just gilding the lily.

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6.  Assess drug-resistance phenotypes, not just genotypes.

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Review 8.  Emerging and evolving concepts in gene essentiality.

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9.  Coalescent framework for prokaryotes undergoing interspecific homologous recombination.

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10.  WGS to predict antibiotic MICs for Neisseria gonorrhoeae.

Authors:  David W Eyre; Dilrini De Silva; Kevin Cole; Joanna Peters; Michelle J Cole; Yonatan H Grad; Walter Demczuk; Irene Martin; Michael R Mulvey; Derrick W Crook; A Sarah Walker; Tim E A Peto; John Paul
Journal:  J Antimicrob Chemother       Date:  2017-07-01       Impact factor: 5.790

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