Bacterial Population Genomics

Corander, Jukka; Croucher, Nicholas J.; Harris, Simon R.; Lees, John A.; Tonkin‐Hill, Gerry

doi:10.1002/9781119487845.ch36

Cited by 9 publications

(9 citation statements)

References 103 publications

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“…The LMM is an extension of linear regression, which allows the inclusion of both fixed and random effects as covariates. By using a kinship matrix to model the variance of a random effect, LMMs consider the genetic relationships between all samples rather than selecting a proportion of the population structure (as in the cluster-based and dimensionality-reduction approaches described above) and has been shown to control type I error without loss of power (35). GEMMA (36) and FaST-LMM (37) are two popular LMM-based GWAS methods and have been used in recent bacterial GWASs, either as standalone methods, or as implemented in BugWAS (23), DBGWAS (24), or pyseer (34).…”

Section: Resultsmentioning

confidence: 99%

“…Promising methods include phyC (9) and treeWAS (15) which detect homoplastic (convergent) mutations along a clonal phylogeny, providing strong control over population structure given accurate phylogenetic tree. Due to the high computational burden of these tests, especially with large sample sizes (35), we did not evaluate these methods here. Moreover, although homoplasies do occur in our simulations (Figure 1b), their rate is not explicitly controlled and thus their impact is hard to assess.…”

Section: Discussionmentioning

confidence: 99%

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Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes

Saber

Shapiro

2019

Preprint

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11Genome Wide Association Studies (GWASs) have the potential to reveal the genetics of microbial 12 phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial 13 sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring 14 spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-15 wide linkage, making it challenging to separate true "hits" (i.e. mutations that cause a phenotype) 16 from non-causal linked mutations. GWAS methods attempt to correct for population structure in 17 different ways, but their performance has not yet been systematically evaluated. Here we developed a 18 bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of 19 mutation, recombination, and other evolutionary parameters, along with a subset of causal mutations 20 underlying a phenotype of interest. We assessed the performance (recall and precision) of three 21 widely-used univariate GWAS approaches (cluster-based, dimensionality-reduction, and linear mixed 22 models, implemented in PLINK, pySEER, and GEMMA) and one relatively new whole-genome 23 elastic net model implemented in pySEER, across a range of simulated sample sizes, recombination 24 rates, and causal mutation effect sizes. As expected, all methods performed better with larger sample 25 sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to 26 correct for population structure were considerably variable according to the choice of parameters. 27 Notably, the elastic net whole-genome model was consistently amongst the highest-performing 28 methods and had the highest power in detecting causal variants with both low and high effect sizes. 29 Most methods reached good performance (Recall > 0.75) to identify causal mutations of strong effect 30 size (log Odds Ratio >= 2) with a sample size of 2000 genomes. However, only elastic nets reached 31 reasonable performance (Recall = 0.35) for detecting markers with weaker effects (log OR ~1) in 32 smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-33 wide linkage, relative to univariate models. However, all methods performed relatively poorly on 34 highly clonal (low-recombining) genomes, suggesting room for improvement in method development. 35 These findings show the potential for whole-genome models to improve bacterial GWAS 36 3 performance. BacGWASim code and simulated data are publicly available to enable further 37 comparisons and benchmarking of new methods. 38 39 Author summary: 40 Microbial populations contain measurable phenotypic differences with important clinical and 41 environmental consequences, such as antibiotic resistance, virulence, host preference and 42 transmissibility. A major challenge is to discover the genes and mutations in bacterial genomes that 43 control these phenotypes. Bacterial Genome-Wide Association Studies (GWASs) are family of 44 methods to statistically assoc...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes

Saber

Shapiro

2019

Preprint

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“…Levels of recombination vary over bacterial species, but in general asexual reproduction leads to strong population structure, which is challenging for association analyses [22,23]. Population structure refers to groups of individuals (sub-populations)…”

Section: Population Structure Phylogeny and Clusteringmentioning

confidence: 99%

Genome-wide association, prediction and heritability in bacteria

Mallawaarachchi

Tonkin‐Hill

Croucher

et al. 2021

Preprint

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Advances in whole-genome genotyping and sequencing have allowed genome-wide analyses of association, prediction and heritability in many organisms. However, the application of such analyses to bacteria is still in its infancy, being limited by difficulties including the plasticity of bacterial genomes and their strong population structure. Here we propose a suite of genome-wide analyses for bacteria that combines methods from human genetics and previous bacterial studies, including linear mixed models, elastic net and LD-score regression. We introduce innovations such as frequency-based allele coding, testing for both insertion/deletion and nucleotide effects and partitioning heritability by genome region. Using a previously-published large cohort study, we analyse three phenotypes of a major human pathogen Streptococcus pneumoniae , including the first analyses of minimum inhibitory concentrations (MIC) for each of two antibiotics, penicillin and ceftriaxone. We show that these are very highly heritable leading to high prediction accuracy, which is explained by many genetic associations identified under good control of population structure effects. In the case of ceftriaxone MIC, these results are surprising because none of the isolates was resistant according to the inhibition zone diameter threshold. We estimate that just over half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes around a quarter of the heritability of ceftriaxone MIC. For the within-host survival phenotype carriage duration, no reliable associations were found but we observed moderate heritability and prediction accuracy, indicating a polygenic trait. While generating important new results for S. pneumoniae , we have critically assessed existing methods and introduced innovations that will be useful for future large-scale population genomics studies to help decipher the genetic architecture of bacterial traits.

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“…Convergence-based methods can yield higher significance with a smaller sample size, but may fail to identify some statistical associations that traditional GWAS approaches would identify when the population is clonal [11]. Additionally, convergence-based methods are limited to smaller datasets because of their large memory requirements and computational time relative to traditional methods [12], but can surmount issues of clonality.…”

Section: Introductionmentioning

confidence: 99%

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

Saund

Snitkin

2020

Microbial Genomics

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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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Bacterial Population Genomics

Cited by 9 publications

References 103 publications

Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes

Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes

Genome-wide association, prediction and heritability in bacteria

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

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