scholarly journals Increased power from bacterial genome-wide association conditional on known effects identifies Neisseria gonorrhoeae macrolide resistance mutations in the 50S ribosomal protein L4

2020 ◽  
Author(s):  
Kevin C Ma ◽  
Tatum D Mortimer ◽  
Marissa A Duckett ◽  
Allison L Hicks ◽  
Nicole E Wheeler ◽  
...  
Keyword(s):  
Ribosomal Protein ◽  
Association Studies ◽  
Bacterial Genome ◽  
Macrolide Resistance ◽  
Genome Wide Association ◽  
Effect Sizes ◽  
23S Rrna ◽  
Genome Wide Association Studies ◽  
Resistance Mutations ◽  
Genome Wide

AbstractThe emergence of resistance to azithromycin complicates treatment of N. gonorrhoeae, the etiologic agent of gonorrhea. Population genomic analyses of clinical isolates have demonstrated that some azithromycin resistance remains unexplained after accounting for the contributions of known resistance mutations in the 23S rRNA and the MtrCDE efflux pump. Bacterial genome-wide association studies (GWAS) offer a promising approach for identifying novel resistance genes but must adequately address the challenge of controlling for genetic confounders while maintaining power to detect variants with lower effect sizes. Compared to a standard univariate GWAS, conducting GWAS conditioned on known resistance mutations with high effect sizes substantially reduced the number of variants that reached genome-wide significance and identified a G70D mutation in the 50S ribosomal protein L4 (encoded by the gene rplD) as significantly associated with increased azithromycin minimum inhibitory concentrations (β = 1.03, 95% CI [0.76, 1.30]). The role and prevalence of these rplD mutations in conferring macrolide resistance in N. gonorrhoeae had been unclear. Here, we experimentally confirmed our GWAS results, identified other resistance-associated mutations in RplD, and showed that in total these RplD binding site mutations are prevalent (present in 5.42% of 4850 isolates) and geographically and temporally widespread (identified in 21/65 countries across two decades). Overall, our findings demonstrate the utility of conditional associations for improving the performance of microbial GWAS and advance our understanding of the genetic basis of macrolide resistance in a prevalent multidrug-resistant pathogen.

scholarly journals Increased power from conditional bacterial genome-wide association identifies macrolide resistance mutations in Neisseria gonorrhoeae

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin C. Ma ◽  
Tatum D. Mortimer ◽  
Marissa A. Duckett ◽  
Allison L. Hicks ◽  
Nicole E. Wheeler ◽  
...  
Keyword(s):  
Neisseria Gonorrhoeae ◽  
Association Studies ◽  
Bacterial Genome ◽  
Etiologic Agent ◽  
Macrolide Resistance ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Resistance Mutations ◽  
Genome Wide ◽  
Azithromycin Resistance

Abstract The emergence of resistance to azithromycin complicates treatment of Neisseria gonorrhoeae, the etiologic agent of gonorrhea. Substantial azithromycin resistance remains unexplained after accounting for known resistance mutations. Bacterial genome-wide association studies (GWAS) can identify novel resistance genes but must control for genetic confounders while maintaining power. Here, we show that compared to single-locus GWAS, conducting GWAS conditioned on known resistance mutations reduces the number of false positives and identifies a G70D mutation in the RplD 50S ribosomal protein L4 as significantly associated with increased azithromycin resistance (p-value = 1.08 × 10−11). We experimentally confirm our GWAS results and demonstrate that RplD G70D and other macrolide binding site mutations are prevalent (present in 5.42% of 4850 isolates) and widespread (identified in 21/65 countries across two decades). Overall, our findings demonstrate the utility of conditional associations for improving the performance of microbial GWAS and advance our understanding of the genetic basis of macrolide resistance.

scholarly journals Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

2015 ◽  
Author(s):  
Guo-Bo Chen ◽  
Sang Hong Lee ◽  
Matthew R Robinson ◽  
Maciej Trzaskowski ◽  
Zhi-Xiang Zhu ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
False Negative ◽  
Genome Wide Association ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Unknown Sample ◽  
Genome Wide

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

scholarly journals Bacterial Genome wide association studies (bGWAS) and transcriptomics identifies cryptic antimicrobial resistance mechanisms in Acinetobacter baumannii

2019 ◽  
Author(s):  
Chandler Roe ◽  
Charles H.D. Williamson ◽  
Adam J. Vazquez ◽  
Kristen Kyger ◽  
Michael Valentine ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Acinetobacter Baumannii ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Bacterial Genome ◽  
Genome Wide Association ◽  
Multiple Drug ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

AbstractAntimicrobial resistance (AMR) in the nosocomial pathogen, Acinetobacter baumannii, is becoming a serious public health threat. While some mechanisms of AMR have been reported, understanding novel mechanisms of resistance is critical for identifying emerging resistance. One of the first steps in identifying novel AMR mechanisms is performing genotype/phenotype association studies. However, performing genotype/phenotype association studies is complicated by the plastic nature of the A. baumannii pan-genome. In this study, we compared the antibiograms of 12 antimicrobials associated with multiple drug families for 84 A. baumannii isolates, many isolated in Arizona, USA. in silico screening of these genomes for known AMR mechanisms failed to identify clear correlations for most drugs. We then performed a genome wide association study (GWAS) looking for associations between all possible 21-mers; this approach generally failed to identify mechanisms that explained the resistance phenotype. In order to decrease the genomic noise associated with population stratification, we compared four phylogenetically-related pairs of isolates with differing susceptibility profiles. RNA-Sequencing (RNA-Seq) was performed on paired isolates and differentially expressed genes were identified. In these isolate pairs, we identified four different potential mechanisms, highlighting the difficulty of broad AMR surveillance in this species. To verify and validate differential expression, amplicon sequencing was performed. These results suggest that a diagnostic platform based on gene expression rather than genomics alone may be beneficial in certain surveillance efforts. The implementation of such advanced diagnostics coupled with increased AMR surveillance will potentially improve A. baumannii infection treatment and patient outcomes.

scholarly journals Genome-wide association studies of global Mycobacterium tuberculosis resistance to thirteen antimicrobials in 10,228 genomes

2021 ◽  
Author(s):  
Sarah G Earle ◽  
Daniel J Wilson ◽  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Health Concern ◽  
Patient Treatment ◽  
23S Rrna ◽  
Cross Resistance ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Repurposed Drugs

The emergence of drug resistant tuberculosis is a major global public health concern that threatens the ability to control the disease. Whole genome sequencing as a tool to rapidly diagnose resistant infections can transform patient treatment and clinical practice. While resistance mechanisms are well understood for some drugs, there are likely many mechanisms yet to be uncovered, particularly for new and repurposed drugs. We sequenced 10,228 Mycobacterium tuberculosis (MTB) isolates worldwide and determined the minimum inhibitory concentration (MIC) on a grid of twofold concentration dilutions for 13 antimicrobials using quantitative microtiter plate assays. We performed oligopeptide- and oligonucleotide-based genome-wide association studies using linear mixed models to discover resistance-conferring mechanisms not currently catalogued. Use of MIC over binary resistance phenotypes increased heritability for the new and repurposed drugs by 26-37%, increasing our ability to detect novel associations. For all drugs, we discovered uncatalogued variants associated with MIC, including in the Rv1218c promoter binding site of the transcriptional repressor Rv1219c (isoniazid), upstream of the vapBC20 operon that cleaves 23S rRNA (linezolid) and in the region encoding an α-helix lining the active site of Cyp142 (clofazimine, all p<10-7.7). We observed that artefactual signals of cross resistance could be unravelled based on the relative effect size on MIC. Our study demonstrates the ability of very large-scale studies to substantially improve our knowledge of genetic variants associated with antimicrobial resistance in M. tuberculosis.

scholarly journals Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes

2020 ◽  
Vol 16 ◽  
pp. 117693432094493
Author(s):  
Yi-Pin Lai ◽  
Thomas R Ioerger
Keyword(s):  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Clinical Population ◽  
Genome Wide Association Studies ◽  
Resistance Mutations ◽  
Association Testing ◽  
Hypervariable Regions ◽  
Genome Wide ◽  
Or Gene

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

scholarly journals A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events

PLoS Genetics ◽  
2018 ◽  
Vol 14 (11) ◽  
pp. e1007758 ◽  
Author(s):  
Magali Jaillard ◽  
Leandro Lima ◽  
Maud Tournoud ◽  
Pierre Mahé ◽  
Alex van Belkum ◽  
...  
Keyword(s):  
Association Studies ◽  
Bacterial Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Events

scholarly journals A fast and agnostic method for bacterial genome-wide association studies: bridging the gap between kmers and genetic events

2018 ◽  
Author(s):  
Magali Jaillard ◽  
Leandro Lima ◽  
Maud Tournoud ◽  
Pierre Mahé ◽  
Alex van Belkum ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Genetic Variants ◽  
Association Studies ◽  
Bacterial Species ◽  
Bacterial Genome ◽  
Genome Wide Association ◽  
Compact Set ◽  
Genome Wide Association Studies ◽  
Alignment Free ◽  
Genome Wide

AbstractMotivationGenome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or fine-assessment of marker effect. Recently, alignment-free methods based on kmer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are hard to interpret.MethodsHere, we introduce DBGWAS, an extended kmer-based GWAS method producing interpretable genetic variants associated with pheno-types. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes identified by the association model into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is fast, alignment-free and only requires a set of contigs and phenotypes. It produces annotated subgraphs representing local polymorphisms as well as mobile genetic elements (MGE) and offers a graphical framework to interpret GWAS results.ResultsWe validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa – along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature.ConclusionOur novel method proved its efficiency to retrieve any type of phenotype-associated genetic variant without prior knowledge. All experiments were computed in less than two hours and produced a compact set of meaningful subgraphs, thereby outperforming other GWAS approaches and facilitating the interpretation of the results.AvailabilityOpen-source tool available at https://gitlab.com/leoisl/dbgwas

scholarly journals hogwash: Three Methods for Genome-Wide Association Studies in Bacteria

2020 ◽  
Author(s):  
Katie Saund ◽  
Evan S Snitkin
Keyword(s):  
Phenotypic Variation ◽  
Phylogenetic Signal ◽  
Association Studies ◽  
Bacterial Genome ◽  
Simulated Data ◽  
R Package ◽  
Genomic Variation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene- or pathway-analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

scholarly journals Hogwash: three methods for genome-wide association studies in bacteria

2020 ◽  
Vol 6 (11) ◽  
Author(s):  
Katie Saund ◽  
Evan S. Snitkin
Keyword(s):  
Phenotypic Variation ◽  
Phylogenetic Signal ◽  
Association Studies ◽  
Bacterial Genome ◽  
Simulated Data ◽  
R Package ◽  
Genomic Variation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Bacterial genome-wide association studies (bGWAS) capture associations between genomic variation and phenotypic variation. Convergence-based bGWAS methods identify genomic mutations that occur independently multiple times on the phylogenetic tree in the presence of phenotypic variation more often than is expected by chance. This work introduces hogwash, an open source R package that implements three algorithms for convergence-based bGWAS. Hogwash additionally contains two burden testing approaches to perform gene or pathway analysis to improve power and increase convergence detection for related but weakly penetrant genotypes. To identify optimal use cases, we applied hogwash to data simulated with a variety of phylogenetic signals and convergence distributions. These simulated data are publicly available and contain the relevant metadata regarding convergence and phylogenetic signal for each phenotype and genotype. Hogwash is available for download from GitHub.

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