We are recruiting for a Postdoctoral Scientist in Statistical Genomics
working on Antimicrobial Resistance (AMR) gene discovery and focused on
Tuberculosis. This will be a joint position at the University of Oxford between Derrick Crook's group and mine, and part of the large international CRyPTIC consortium.
The role is for a population geneticist or statistical geneticist to
develop and apply statistical methods, including genome-wide association studies, for discovering rare and common genetic variants underlying
antimicrobial resistance in Mycobacterium tuberculosis.
One third of the world's population - 2.5 billion people - are thought to be infected with tuberculosis (TB). This post offers an opportunity to work with global TB experts from five continents, statistical
geneticists, clinicians, medical statisticians and software engineers;
integrating statistical genetics, bioinformatics and machine learning
methods with the aim of uncovering all genomic variants causing at least
1% resistance to first line anti-TB drugs.
We're looking for candidates with a PhD in genomics, evolutionary biology, statistics or a
related subject. The post is full-time and fixed-term for up to 3 years initially.
The deadline for applications is noon on Friday 6th May 2016.
Tuesday, 12 April 2016
Thursday, 7 April 2016
Making the most of bacterial GWAS: new paper in Nature Microbiology
In a new paper published this week in Nature Microbiology, we report the performance of genome wide association studies (GWAS) in bacteria to identify causal mechanisms of antibiotic resistance in four major pathogens, and introduce a new method, bugwas, to make the most of bacterial GWAS for traits under less strong selection.
As explained by Sarah Earle, joint first author with Jessie Wu and Jane Charlesworth, the problem with GWAS in bacteria is strong population structure and the consequent strong coinheritance of genetic variants throughout the genome. This phenomenon - known as genome-wide linkage disequilibrium (LD) - comes about because exchange of genes is relatively infrequent in bacteria, which reproduce clonally, compared to organisms that exchange genes every generation through sexual reproduction.
Genome-wide LD makes it difficult for GWAS to distinguish variants that causally influence a trait from other, coinherited variants that have no direct effect on the trait.
In the case of antibiotic resistance - a trait of high importance to human health - bacteria are under extraordinary selection pressures because resistance is a matter of life and death, to them as well as their human host. This helps overcome coinheritance and pinpoint causal variants because antibiotic usage selects for the independent evolution of the same resistance-causing variants in different genetic backgrounds.
Consequently, bacterial GWAS works very efficiently for antibiotic resistance: the variants most significantly associated with antibiotic resistance in 26 out of the 27 GWAS we performed were genuine resistance-conferring mutations. In the 27th we uncovered a putative novel mechanism of resistance to cefazolin in E. coli. These results for 17 antibiotics (ampicillin, cefazolin, cefuroxime, ceftriaxone, ciprofloxacin, erythromycin, ethambutol, fusidic acid, gentamicin, isoniazid, penicillin, pyrazinamide, methicillin, rifampicin, tetracycline, tobramycin and trimethoprim) across four species (E. coli, K. pneumoniae, M. tuberculosis and S. aureus) build on earlier work investigating beta-lactam resistance in S. pneumoniae, and convincingly demonstrate the potential for bacterial GWAS to discover new genes underlying important traits under strong selection.
What about traits under less strong selection, which probably includes pretty much every other bacterial trait? We show in this context that coinheritance poses a major challenge, based on detailed simulations. Often it may not be possible to use GWAS to pinpoint individual variants responsible for different traits because they are coinherited with - possibly many - other uninvolved variants.
But all is not lost. We show that even when individual locus-level effects cannot be pinpointed, there is often excellent power to characterize lineage-level differences in phenotype between strains. This is helpful for multiple reasons: (1) we often conceptualize trait variability in bacteria at the level of strain-to-strain differences (2) these differences can be highly predictive (3) we can prioritize variants for functional follow-up based on their contribution to strain-level differences.
These concepts represent a substantial departure from regular GWAS. In the human setting for instance, lineage-level differences are usually discarded as uninteresting or artefactual, and variants are almost always prioritized based on statistical evidence for involvement over-and-above any contribution to lineage-level differences. In the bacterial setting, we are forced to depart from these conventions because a large proportion of all genetic variation is strongly strain-stratified. To find out more, see the paper and try our methods.
As explained by Sarah Earle, joint first author with Jessie Wu and Jane Charlesworth, the problem with GWAS in bacteria is strong population structure and the consequent strong coinheritance of genetic variants throughout the genome. This phenomenon - known as genome-wide linkage disequilibrium (LD) - comes about because exchange of genes is relatively infrequent in bacteria, which reproduce clonally, compared to organisms that exchange genes every generation through sexual reproduction.
Genome-wide LD makes it difficult for GWAS to distinguish variants that causally influence a trait from other, coinherited variants that have no direct effect on the trait.
In the case of antibiotic resistance - a trait of high importance to human health - bacteria are under extraordinary selection pressures because resistance is a matter of life and death, to them as well as their human host. This helps overcome coinheritance and pinpoint causal variants because antibiotic usage selects for the independent evolution of the same resistance-causing variants in different genetic backgrounds.
Consequently, bacterial GWAS works very efficiently for antibiotic resistance: the variants most significantly associated with antibiotic resistance in 26 out of the 27 GWAS we performed were genuine resistance-conferring mutations. In the 27th we uncovered a putative novel mechanism of resistance to cefazolin in E. coli. These results for 17 antibiotics (ampicillin, cefazolin, cefuroxime, ceftriaxone, ciprofloxacin, erythromycin, ethambutol, fusidic acid, gentamicin, isoniazid, penicillin, pyrazinamide, methicillin, rifampicin, tetracycline, tobramycin and trimethoprim) across four species (E. coli, K. pneumoniae, M. tuberculosis and S. aureus) build on earlier work investigating beta-lactam resistance in S. pneumoniae, and convincingly demonstrate the potential for bacterial GWAS to discover new genes underlying important traits under strong selection.
What about traits under less strong selection, which probably includes pretty much every other bacterial trait? We show in this context that coinheritance poses a major challenge, based on detailed simulations. Often it may not be possible to use GWAS to pinpoint individual variants responsible for different traits because they are coinherited with - possibly many - other uninvolved variants.
But all is not lost. We show that even when individual locus-level effects cannot be pinpointed, there is often excellent power to characterize lineage-level differences in phenotype between strains. This is helpful for multiple reasons: (1) we often conceptualize trait variability in bacteria at the level of strain-to-strain differences (2) these differences can be highly predictive (3) we can prioritize variants for functional follow-up based on their contribution to strain-level differences.
These concepts represent a substantial departure from regular GWAS. In the human setting for instance, lineage-level differences are usually discarded as uninteresting or artefactual, and variants are almost always prioritized based on statistical evidence for involvement over-and-above any contribution to lineage-level differences. In the bacterial setting, we are forced to depart from these conventions because a large proportion of all genetic variation is strongly strain-stratified. To find out more, see the paper and try our methods.
Subscribe to:
Posts (Atom)
Posts
