Royal Society Summer Science Exhibition 2020

This year the Royal Society's Summer Science Exhibition was online, and included highlights and updates from previous exhibitors, among them ours from 2018. This video was posted on Tuesday's session:

New paper: GenomegaMap for dN/dS in over 10,000 genomes

Published this week in Molecular Biology and Evolution, is a new paper joint with the CRyPTIC Consortium "GenomegaMap: within-species genome-wide d_N/d_S estimation from over 10,000 genomes".

The d_N/d_S ratio is a popular statistic in evolutionary genetics that quantifies the relative rates of protein-altering and non-protein-altering mutations. The rate is adjusted so that under neutral evolution - i.e. when the survival and reproductive advantage of all variants is the same - it equals 1. Typically, d_N/d_S is observed to be less than 1 meaning that new mutations tend to be disfavoured, implying they are harmful to survival or reproduction. Occasionally, d_N/d_S is observed to be greater than 1 meaning that new mutations are favoured, implying they provide some survival or reproductive advantage. The aim of estimating d_N/d_S is usually to identify mutations that provide an advantage.

Theoreticians are often critical of d_N/d_S because it is more of a descriptive statistic than a process-driven model of evolution. This overlooks the problem that currently available models make simplifying assumptions such as minimal interference between adjacent mutations within genes. These assumptions are not obviously appropriate in many species, including infectious micro-organisms, that exchange genetic material infrequently.

There are many methods for measuring d_N/d_S. This new paper overcomes two common problems:

• It is fast no matter how many genomes are analysed together.
• It is robust whether there is frequent genetic exchange (which causes phylogenetic methods to report spurious signals of advantageous mutation) or infrequent genetic exchange.

The paper includes detailed simulations that establish the validity of the approach, and it goes on to demonstrate how genomegaMap can detect advantageous mutations in 10,209 genomes of Mycobacterium tuberculosis, the bacterium that causes tuberculosis. The method reproduces known signals of advantageous mutations that make the bacteria resistant to antibiotics, and it discovers a new signal of advantageous mutations in a cold-shock protein called deaD or csdA.

Software that implements genomegaMap is available on Docker Hub and the source code and documentation are available on Git Hub.

With the steady rise of more and more genome sequences, the analysis of data becomes an increasing challenge even with modern computers, so it is hoped that this new method provides a useful way to exploit the opportunities in such large datasets to gain new insights into evolution.

Postdoc Available in Statistical Genetics

The closing date for applications for this post is noon on Wednesday 15th April 2020.

We are seeking an exceptional researcher with a track record in methods development for Statistical Genomics and an interest in Infectious Disease to join our group at the Big Data Institute. Our research focuses on Bacterial Genomics, Genome-Wide Association Studies and Population Genetics. The aim of the post is to conduct innovative research within the group's range of interests and to make use of the opportunities afforded by our outstanding collaborators. We welcome candidates who wish to use the opportunity as a stepping stone to independent funding.

The Oxford University Big Data Institute (BDI) is an interdisciplinary research centre aiming to develop, evaluate and deploy efficient methods for acquiring and analysing biomedical data at scale and for exploiting the opportunities arising from such studies. The Nuffield Department of Population Health, a partner in the BDI, contains world-renowned population health research groups and is an excellent environment for multi-disciplinary teaching and research.

The Postdoctoral Researcher in Statistical Genomics will join our team which has expertise in microbiology, genomics, evolution, population genetics and statistical inference. Responsibilities include planning a research project and milestones with help and guidance from the group, preparing manuscripts for publication, keeping records of results and methods and tracking milestones, and disseminating results.

To be considered, you need to hold, or be close to completion of, a PhD/DPhil involving statistical methods development. You also need experience of large-scale statistical data analysis, evidence of originating and executing your own academic research ideas and excellent interpersonal skills and the ability to work closely with others in a team.

For informal enquiries, please contact me.

Further details, including how to apply are here: https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=145506

Correction published and R package on GitHub

The correction to "The harmonic mean p-value for combining dependent tests" has been published www.pnas.org/content/early/2019/10/02/1914128116 and the main article has been corrected online www.pnas.org/content/116/4/1195.

I have posted the source code for the harmonicmeanp R package on GitHub. This means there is now a development version with the latest updates, and instructions for installing it: github.com/danny-wilson/harmonicmeanp/tree/dev.

Updated correction: The harmonic mean p-value for combining dependent tests

— To view a PDF version of this announcement click here —

Important: Please update to version 3.0 of the harmonicmeanp R package to address critical errors in the main function p.hmp.

I would like to update the correction I issued on July 3, 2019 to cover a second error I discovered that affects the main function of the R package,  p.hmp. There are two errors in the original paper:

1. The paper (Wilson 2019 PNAS 116: 1195-1200) erroneously stated that the test \(\overset{\circ}{p}_\mathcal{R} \leq \alpha_{|\mathcal{R}|}\,w_\mathcal{R}\) controls the strong-sense family-wise error rate asymptotically when it should read \(\overset{\circ}{p}_\mathcal{R} \leq \alpha_{L}\,w_\mathcal{R}\).
2. The paper incorrectly stated that one can produce adjusted p-values that are asymptotically exact, as intended in the original Figure 1, by transforming the harmonic mean p-value with Equation 4 before adjusting by a factor \(1/w_{\mathcal{R}}\). In fact the harmonic mean p-value must be multiplied by \(1/w_{\mathcal{R}}\) before transforming with Equation 4.

The p.hmp function prior to version 3.0 of the R package was affected by these errors.

In the above,

• L is the total number of individual p-values.
• \(\mathcal{R}\) represents any subset of those p-values.
• \(\overset{\circ}{p}_\mathcal{R} = \left(\sum_{i\in\mathcal{R}} w_i\right)/\left(\sum_{i\in\mathcal{R}} w_i/p_i\right)\) is the HMP for subset \(\mathcal{R}\).
• \(w_i\) is the weight for the ith p-value. The weights must sum to one: \(\sum_{i=1}^L w_i=1\). For equal weights, \(w_i=1/L\).
• \(w_\mathcal{R}=\sum_{i\in\mathcal{R}}w_i\) is the sum of weights for subset \(\mathcal{R}\).
• \(|\mathcal{R}|\) gives the number of p-values in subset \(\mathcal{R}\).
• \(\alpha_{|\mathcal{R}|}\) and \(\alpha_{L}\) are significance thresholds provided by the Landau distribution (Table 1).

Version 2.0 (released July 2019) of the harmonicmeanp R package only addressed the first error, which is why I am now releasing version 3.0 to address both errors. Compared to version 1.0, the main function p.hmp has been updated to take an additional argument, L, which sets the total number of p-values. If argument L is omitted, a warning is issued and L is assumed to equal the length of the first argument, p, preserving earlier behaviour. Please update the R package to version 3.0.

The tutorial, available as a vignette in the R package and online, is affected quantitatively by both errors, and has been extensively updated for version 3.0.

The second error affects only one line of the corrected paper (issued July 2019). I have updated it to address the second error and two typos in Figure legends 1 and 2: http://www.danielwilson.me.uk/files/wilson_2019_annotated_corrections.v2.pdf. You will need Adobe Reader to properly view the annotations and the embedded corrections to Figures 1 and 2.

I would like to deeply apologise to users for the inconvenience the two errors have caused.

More information follows under the headings:

• Why does this matter?
• How does it affect the paper?
• Where did the errors come from?
• How do I update the R package?
• What if I have already reported results?

Why does this matter?

The family-wise error rate (FWER) controls the probability of falsely rejecting any null hypotheses, or groups of null hypotheses, when they are true. The strong-sense FWER maintains control even when some null hypotheses are false, thereby offering control across much broader and more relevant scenarios than the weak-sense FWER.

The ssFWER is not controlled at the expected rate if:

1. The more lenient threshold \(\alpha_{|\mathcal{R}|}\) is used rather than the corrected threshold \(\alpha_L\), both derived via Table 1 of the paper from the desired ssFWER \(\alpha\).
2. Raw p-values are transformed with Equation 4 before adjusting by a factor \(w_{\mathcal{R}}^{-1}\), rather than adjusting the raw p-values by a factor \(w_{\mathcal{R}}^{-1}\) before transforming with Equation 4.

The p.hmp function of the R package suffers both issues in version 1.0, and the second issue in version 2.0. Please update to version 3.0.

Tests in which significance is marginal or non-significant at \(\alpha=0.05\) are far more likely to be affected in practice.

Regarding error 1, individual p-values need to be assessed against the threshold \(\alpha_{L}/L\) when the HMP is used, not the more lenient \(\alpha_{1}/L\) nor the still more lenient \(\alpha/L\) (assuming equal weights). This shows that there is a cost to using the HMP compared to Bonferroni correction in the evaluation of individual p-values (and indeed small groups of p-values). For one billion tests \(\left(L=10^9\right)\) and a desired ssFWER of \(\alpha=0.01\), the fold difference in thresholds from Table 1 would be \(\alpha/\alpha_L=0.01/0.008=1.25\).

However, it remains the case that HMP is more powerful than Bonferroni for assessing the significance of large groups of hypotheses. This is the motivation for using the HMP, and combined tests in general, because the power to find significant groups of hypotheses will be higher than the power to detect significant individual hypotheses when the total number of tests (L) is large and the aim is to control the ssFWER.

How does it affect the paper?

I have submitted a request to correct the paper to PNAS. It is up to the editors whether to agree to this request. A copy of the published paper, annotated with the requested corrections, is available here: http://www.danielwilson.me.uk/files/wilson_2019_annotated_corrections.v2.pdf. Please use Adobe Reader to properly view the annotations and the embedded corrections to Figures 1 and 2.

Where did the errors come from?

Regarding the first error, page 11 of the supplementary information gave a correct version of the full closed testing procedure that controls the ssFWER (Equation 37). However, it went on to erroneously claim that "one can apply weighted Bonferroni correction to make a simple adjustment to Equation 6 by substituting \(\alpha_{|\mathcal{R}|}\) for \(\alpha\)." This reasoning would only be valid if the subsets of p-values to be combined were pre-selected and did not overlap. However, this would no longer constitute a flexible multilevel test in which every combination of p-values can be tested while controlling the ssFWER. The examples in Figures 1 and 2 pursued multilevel testing, in which the same p-values were assessed multiple times in subsets of different sizes, and in partially overlapping subsets of equal sizes. For the multilevel test, a formal shortcut to Equation 37, which makes it computationally practicable to control the ssFWER, is required. The simplest such shortcut procedure is the corrected test

$$\overset{\circ}{p}_\mathcal{R} \leq \alpha_{L}\,w_\mathcal{R}$$

One can show this is a valid multilevel test because if $$\overset{\circ}{p}_\mathcal{R}\leq\alpha_L\,w_\mathcal{R}$$ then $$\overset{\circ}{p}=\left(w_\mathcal{R}\,\overset{\circ}{p}^{-1}_\mathcal{R}+w_{\mathcal{R}^\prime}\,\overset{\circ}{p}^{-1}_{\mathcal{R}^\prime}\right)^{-1} \leq w^{-1}_\mathcal{R}\,\overset{\circ}{p}_\mathcal{R}\leq\alpha_L$$an argument that mirrors the logic of Equation 7 for direct interpretation of the HMP (an approximate procedure), which is not affected by this correction.

The second error, which was also caused by carelessness on my part, occurred in the main text in the statement "(Equivalently, one can compare the exact p-value from Eq. 4 with \(\alpha\,w_{\mathcal{R}}\).)" I did not identify it sooner because the corrected version of the paper no longer uses Equation 4 to transform p-values in Figure 1.

How do I update the R package?

The R package is maintained at https://cran.r-project.org/package=harmonicmeanp.

In R, test whether you have version 3.0 of the package installed as follows:

packageVersion("harmonicmeanp")

The online binaries take a few days or weeks to update, so to ensure you install the most recent version of the package install from source by typing:

install.packages("harmonicmeanp", dependencies=TRUE, type="source")

You can additionally specify the CRAN repository for example:

install.packages("harmonicmeanp", dependencies=TRUE, type="source", repos="https://cran.wu.ac.at")

After installation, check again the version number:

stopifnot(packageVersion("harmonicmeanp")>=3.0)

What if I have already reported results?

I am very sorry for inconvenience caused in this case.

As long as the 'headline' test was significant with p.hmp under R package versions 1.0 or 2.0, then the weak-sense FWER can be considered to have been controlled. The 'headline' test is the test in which all p-values are included in a single combined test. The headline test is not affected by either error, because \(|\mathcal{R}|=L\) and \(w_{\mathcal{R}}=1\). The headline test controls the weak-sense FWER, and therefore so does a two-step procedure in which subsets are only deemed significant when the headline test is significant (Hochberg and Tamhane, 1987, Multiple Comparison Procedures, p. 3, Wiley).

If the headline test was not significant, re-running the analysis with version 3.0 will not produce significant results either because the stringency is greater for controlling the strong-sense FWER. If the headline test was significant, you may wish to reanalyse the data with version 3.0 to obtain strong-sense FWER control, because this was the criterion the HMP procedure was intended to control.

If some results that were significant under version 1.0 or 2.0 of the R package are no longer significant, you may conclude they are not significant or you may report them as significant subject to making clear that only the weak-sense FWER was controlled.

More information

For more information please leave a comment below, or get in touch via the contact page.

Correction: The harmonic mean p-value for combining dependent tests

Important: this announcement has been superceded. Please see updated correction. 

I would like to issue the following correction to users of the harmonic mean p-value (HMP), with apologies: The paper (Wilson 2019 PNAS 116: 1195-1200) erroneously states that the following asymptotically exact test controls the strong-sense family-wise error rate for any subset of p-values \(\mathcal{R}\):
$$\overset{\circ}{p}_\mathcal{R} \leq \alpha_{|\mathcal{R}|}\,w_\mathcal{R}$$
when it should read
$$\overset{\circ}{p}_\mathcal{R} \leq \alpha_{L}\,w_\mathcal{R}$$

where:

• L is the total number of individual p-values.
• \(\mathcal{R}\) represents any subset of those p-values.
• \(\overset{\circ}{p}_\mathcal{R} = \left(\sum_{i\in\mathcal{R}} w_i\right)/\left(\sum_{i\in\mathcal{R}} w_i/p_i\right)\) is the HMP for subset \(\mathcal{R}\).
• \(w_i\) is the weight for the ith p-value. The weights must sum to one: \(\sum_{i=1}^L w_i=1\). For equal weights, \(w_i=1/L\).
• \(w_\mathcal{R}=\sum_{i\in\mathcal{R}}w_i\) is the sum of weights for subset \(\mathcal{R}\).
• \(|\mathcal{R}|\) gives the number of p-values in subset \(\mathcal{R}\).
• \(\alpha_{|\mathcal{R}|}\) and \(\alpha_{L}\) are significance thresholds provided by the Landau distribution (Table 1).

In version 2.0 of the harmonicmeanp R package, the main function p.hmp is updated to take an additional argument, L, which sets the total number of p-values. If argument L is omitted, a warning is issued and L is assumed to equal the length of the first argument, p, preserving previous behaviour. Please update the R package.

An updated tutorial is available as a vignette in the R package and online here: http://www.danielwilson.me.uk/harmonicmeanp/hmpTutorial.html

Why does this matter?

The family-wise error rate (FWER) controls the probability of falsely rejecting any null hypotheses, or groups of null hypotheses, when they are true. The strong-sense FWER maintains control even when some null hypotheses are false, thereby offering control across much broader and more relevant scenarios.

Using the more lenient threshold \(\alpha_{|\mathcal{R}|}\) rather than the corrected threshold \(\alpha_L\), both derived via Table 1 of the paper from the desired ssFWER \(\alpha\), means the ssFWER is not controlled at the expected rate.

Tests with small numbers of p-values are far more likely to be affected in practice. In particular, individual p-values should be assessed against the threshold \(\alpha_{L}/L\) when the HMP is used, not the more lenient \(\alpha_{1}/L\) nor the still more lenient \(\alpha/L\) (assuming equal weights). This shows that there is a cost to using the HMP compared to Bonferroni correction in the evaluation of individual p-values. For one billion tests \(\left(L=10^9\right)\) and a desired ssFWER of \(\alpha=0.01\), the fold difference in thresholds from Table 1 would be \(\alpha/\alpha_L=0.01/0.008=1.25\).

However, it remains the case that HMP is much more powerful than Bonferroni for assessing the significance of groups of hypotheses. This is the motivation for using the HMP, and combined tests in general, because the power to find significant groups of hypotheses will be much higher than the power to detect significant individual hypotheses when the total number of tests (L) is large and the aim is to control the ssFWER.

How does it affect the paper?

I have submitted a request to correct the paper to PNAS. It is up to the editors whether to agree to this request. A copy of the published paper, annotated with the requested corrections, is available here: http://www.danielwilson.me.uk/files/wilson_2019_annotated_corrections.pdf. Please use Adobe Reader to properly view the annotations and the embedded corrections to Figures 1 and 2.

Where did the error come from?

Page 11 of the supplementary information gave a correct version of the full closed testing procedure that controls the ssFWER (Equation 37). However, it went on to erroneously claim that "one can apply weighted Bonferroni correction to make a simple adjustment to Equation 6 by substituting \(\alpha_{|\mathcal{R}|}\) for \(\alpha\)." This reasoning would only be valid if the subsets of p-values to be combined were pre-selected and did not overlap. However, this would no longer constitute a flexible multilevel test in which every combination of p-values can be tested while controlling the ssFWER. The examples in Figures 1 and 2 pursued multilevel testing, in which the same p-values were assessed multiple times in subsets of different sizes, and in partially overlapping subsets of equal sizes. For the multilevel test, a formal shortcut to Equation 37, which makes it computationally practicable to control the ssFWER, is required. The simplest such shortcut procedure is the corrected test

$$\overset{\circ}{p}_\mathcal{R} \leq \alpha_{L}\,w_\mathcal{R}$$

One can show this is a valid multilevel test because if $$\overset{\circ}{p}_\mathcal{R}\leq\alpha_L\,w_\mathcal{R}$$ then $$\overset{\circ}{p}=\left(w_\mathcal{R}\,\overset{\circ}{p}^{-1}_\mathcal{R}+w_{\mathcal{R}^\prime}\,\overset{\circ}{p}^{-1}_{\mathcal{R}^\prime}\right)^{-1} \leq w^{-1}_\mathcal{R}\,\overset{\circ}{p}_\mathcal{R}\leq\alpha_L$$an argument that mirrors the logic of Equation 7 for direct interpretation of the HMP, which is not affected by this correction.

More information

For more information please leave a comment below, or get in touch via the contact page.

New paper: PVL toxin associated with pyomyositis

In a new collaborative study published this week in eLife, we report a strong association between Staphylococcus aureus that carry the PVL toxin and pyomyositis, a muscle infection often afflicting children in the tropics.

Catrin Moore and colleagues at the Angkor Children's Hospital in Siem Reap, Cambodia, spent more than a decade collecting S. aureus bacteria from pyomyositis infections in young children, and built a comparable control group of S. aureus carried asymptomatically in children of similar age and location.

When Bernadette Young in our group compared the genomes of cases and controls using statistical tools we developed, she found some strong signals:

• Most, but not all, pyomyositis was caused by the CC-121 strain, common in Cambodia.
• The association with CC-121 was driven by the PVL toxin which it carries.

The ability to pinpoint the association to PVL came about because (i) a sub-group of CC-121 that lacked PVL caused no pyomyositis and (ii) pyomyositis-causing S. aureus from backgrounds that rarely caused pyomyositis were unusual in also possessing PVL.

The strength of the PVL-pyomyositis association was extraordinarily strong, so strong that PVL appeared all-but necessary for disease. Moreover, disease appeared to be monogenic, with no other genes involved elsewhere in the bacterial genome. To discover an apparently monogenic disease mechanism for a common disease is very unusual nowadays.

The discovery has immediate practical implications because it draws parallels between pyomyositis and toxin-driven bacterial diseases like tetanus and diphtheria that have proven amenable to immunization. The fact that anti-PVL vaccines have already been developed in other contexts offers hope for the future treatment of this debilitating tropical infection.

Our study throws much-needed light on a subject that has been the subject of heated debate over previous years. Many bacterial toxins, PVL included, have been implicated in diverse S. aureus disease manifestations, often without sound evidence. Because PVL is known to contribute to angry, pus-filled skin infections, and has been observed in bacteria causing rare and severe S. aureus infections, some authors have implicated it in dangerous diseases including necrotizing pneumonia, septic arthritis and pyomyositis, but detailed meta-analyses have dismissed these claims as not substantiated. Our GWAS approach offers unprecedented robustness over previous generations of candidate gene studies by accounting for bacterial genetic variation across the entire genome.

If you are interested, please take a closer look at the paper.

New paper in PNAS: harmonic mean p-value

Published on Friday in Proceedings of the National Academy of Sciences USA, "The harmonic mean p-value for combining dependent tests" reports a new method for performing combined tests. A revised R package with detailed examples is now available online as the harmonicmeanp package on CRAN.

The method has two stages:

• Compute a test statistic: the harmonic mean of the p-values (HMP) of the tests to be combined. Remarkably, this HMP is itself a valid p-value for small values (e.g. below 0.05).
• Calculate an asymptotically exact p-value from the test statistic using generalized central limit theorem. The distribution is a type of Stable distribution first described by Lev Landau.

The method, which controls the strong-sense family-wise error rate (ssFWER), has several advantages over existing alternatives to combining p-values:

• Combining p-values allows information to be aggregated over multiple tests and requires less stringent significance thresholds.
• The HMP procedure is robust to positive dependence between the p-values, making it more widely applicable than Fisher's method which assumes independence.
• The HMP procedure is more powerful than the Bonferroni and Simes procedures.
• The HMP procedure is more powerful than the Benjamini-Hochberg (BH) procedure, even though BH only controls the weaker false discovery rate (FDR) and weak-sense family-wise error rate (wsFWER) in the sense that whenever the BH procedure detects one or more significant p-values, the HMP procedure will detect one or more significant p-values or groups of significant p-values.

The ssFWER can be considered gold-standard control of false positives because it aims to control the probability of one or more false positives even in the presence true positives. The HMP is inspired by Bayesian model averaging and approximates a model-averaged Bayes factor under certain conditions.

In researching and revising the paper, I looked high and low for previous uses of the harmonic-mean p-value because most ideas have usually been had already. Although there is a class of methods that use different types of average p-value (without compelling motivation), I did not find a precedent. Until today, a few days too late, so I may as well get in there and declare it before anyone else. I. J. Good published a paper in 1958 that mysteriously appeared when I googled the new publication on what he called the "harmonic mean rule-of-thumb", effectively for model-averaging. Undeniably, I did not do my homework thoroughly enough. Still, I would be interested if others know more about the history of this rule-of-thumb.

Good's paper, available on Jstor, proposes that the HMP "should be regarded as an approximate tail-area probability" [i.e. p-value], although he did not propose the asymptotically exact test (Eq. 4) or the multilevel test procedure (Eq. 6) that are important to my approach. His presentation is amusingly apologetic, e.g. "an approximate rule of thumb is tentatively proposed in the hope of provoking discussion", "this rule of thumb should not be used if the statistician can think of anything better to do" and "The 'harmonic-mean rule of thumb' is presented with some misgivings, because, like many other statistical techniques, it is liable to be used thoughtlessly". Perhaps this is why the method (as far as I could tell) had disappeared from the literature. Hopefully the aspects new to my paper will shake off these misgivings and provide users with confidence that the procedure is interpretable and well-motivated on theoretical as well as empirical grounds. Please give it a read!

Work cited

• R. A. Fisher (1934) Statistical Methods for Research Workers (Oliver and Boyd, Edinburgh), 5th Ed.
• L. D. Landau (1944) On the energy loss of fast particles by ionization. Journal of Physics U.S.S.R. 8: 201-205.
• I. J. Good (1958) Significance tests in parallel and in series. Journal of the American Statistical Association 53: 799-813. (Jstor)
• R. J. Simes (1986) An improved Bonferroni procedure for multiple tests of significance. Biometrika 73: 751-754.
• Y. Benjamini and Y. Hochberg (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289-300.
• D. J. Wilson (2019) The harmonic mean p-value for combining dependent tests. Proceedings of the National Academy of Sciences U.S.A. published ahead of print January 4, 2019. (PNAS)

Bacterial Doubling Times in the Wild

How fast do bacteria grow outside the laboratory? This simple question is very difficult to address directly, because it is near-impossible to track a lineage of bacterial cells, ancestor-to-decendant, inside an infected patient or through a river. Now in new work published in Proceedings B, Beth Gibson, Ed Feil, Adam Eyre-Walker and I exploit genome sequencing to try to get a handle on the problem indirectly.

We have done it by comparing two known quantities and taking the ratio: the rate at which DNA mutates in bacteria per year, and the rate it mutates per replication. This tells us in theory how many replications there are per year.

The mutation rate per replication has long been studied in the laboratory, and is around once per billion letters. Meanwhile, the recent avalanche of genomic data has allowed microbiologists to quantify the rate at which bacteria evolve over short time scales such as a year, including during outbreaks and even within individual infected patients. Most bugs mutate about once per million letters per year, with ten-fold variation above and below this not uncommon among different species.

For five species both these quantities exist. The fastest bug we looked at causes cholera and we estimate it doubles once every hour on average (give or take 30 minutes). The slowest was Salmonella, which we estimate doubles once a day on average (give or take 8 hours). In between were Staph. aureus and Pseudomonas at about two hours each, and E. coli at 15 hours. These are average over the very diverse and often hostile conditions that a bacterial cell may find itself in during the course of its natural lifecycle. To find out more about the work, please check out the paper.

PhD Studentship: Genomic prediction of antimicrobial resistance spread

This position is now closed
An opportunity has arisen for a D.Phil. (Ph.D.) place on the BBSRC-funded Oxford Interdisciplinary Bioscience Doctoral Training Partnership in the area of Artificial Intelligence, specifically Predicting the spread of antimicrobial resistance from genomics using machine learning.

If successful in a competitive application process, the candidate will join a cohort of students enrolled in the DTP’s one-year interdisciplinary training programme, before commencing the research project and joining my research group at the Big Data Institute.

This project addresses the BBSRC priority area “Combatting antimicrobial resistance” by using ML to predict the spread of antimicrobial resistance in human, animal and environmental bacteria exemplified by Escherichia coli. Understanding how quickly antimicrobial resistance (AMR) will spread helps plan effective prevention, improved biosecurity, and strategic investment into new measures. We will develop ML tools for large genomic datasets to predict the future spread of AMR in humans, animals and the environment. The project will create new methods based on award-winning probabilistic ML tools pioneered in my group (BASTA, SCOTTI) by training models using genomic and epidemiological data informative about past spread of AMR. We will apply the tools collaboratively to genomic studies of E. coli in Kenya, the UK and across Europe from humans, animals and the environment, Enterobacteriaceae in North-West England, and Campylobacter in Wales. Genomics has proven effective for asking “what went wrong” in the context of outbreak investigation and AMR spread; here we will address the greater challenge of repurposing such information using ML for forward prediction of future spread of AMR. Scrutiny will be intense because future predictions can and will be tested, raising the bar for the biological realism required while producing computationally efficient tools.

Attributes of suitable applicants: Understanding of genomics. Interest in infectious disease. Some numeracy, e.g. mathematics A-level, desirable. Experience of coding would help.

Funding notes: BBSRC eligibility criteria for studentship funding applies (https://www.ukri.org/files/legacy/news/training-grants-january-2018-pdf/). Successful students will receive a stipend of no less than the standard RCUK stipend rate, currently set at £14,777 per year.

How to apply: send me a CV and brief covering letter/email (no more than 1 page) explaining why you are interested and suitable by the Wednesday 11 July initial deadline. I will invite the best applicant/s to submit with me a formal application in time for the Friday 13 July second-stage deadline.

Royal Society Summer Science Exhibition Stall July 2-8

Next week researchers from the Modernising Medical Microbiology consortium, collaborating groups and I will exhibit the Resistance is Futile stall at the Royal Society Summer Science Exhibition. The exhibition is a free event in central London open to all visitors. Our stall is an opportunity to tell visitors about our research, and how advances in genetics are influencing day-to-day life. On show at the Resistance is Futile stall:

Oxford Nanopore Technology Demos

DNA sequencing in the NHS is shortening the time to diagnose antibiotic resistance in serious infections

Evolution Dance Mat

Resistance mutants arise spontaneously through chance copying errors during DNA replication

Antibiotic Resistance Coconut Shy

Antibiotic use gives resistance mutants a strong advantage so they rapidly increase in frequency.

The exhibition runs from Monday 2 July - Sunday 8 July at Carlton House Terrace, London, SW1Y 5AG. For more information about our stall click here and for general visitor information about the exhibition click here. Please spread the word!

During the exhibition we will be tweeting from @ResistanceIF

Our stall is generously supported by Oxford Nanopore Technology, the Nuffield Department of Medicine, and through public engagement research funding awarded to our research groups by the Wellcome Trust, the Royal Society, the National Institute for Health Research, the Oxford Biomedical Research Centre, the Natural Environment Research Council, the Medical Research Council, the Newton Fund and the Bill & Melinda Gates Foundation.

Postdoc positions in Data Science and Molecular Microbiology

These positions are now closed
As part of the move to the Big Data Institute, two new postdoctoral positions funded by the Robertson Foundation are available in Data Science and Molecular Microbiology.

The BDI is a new interdisciplinary research centre aiming to develop, evaluate and deploy efficient methods for acquiring and analysing biomedical data at scale and for exploiting the opportunities arising from such studies. The BDI is a joint venture between the renowned Nuffield Department of Population Health (NDPH) and NDM.

The Data Scientist role, split between the BDI and London, will be part of a team developing systems for continuous record linkage between Public Health England and other population health records. The aims are to design record linkage algorithms, manage front ends for viewing the data source, and analyse and interpret results. We're looking for a graduate or equivalent experience in computer science, data science, statistics, or any other relevant subject with a strong quantitative component. Knowledge of databases like SQL and computer programming are needed.

The Molecular Microbiology role, based mainly at the John Radcliffe Hospital Microbiology Department, will be part of a team researching Staphylococcus aureus infection using RNA sequencing, genome wide association studies, and biochemical and immunological assays of bacterial behaviour. The aims include designing microbiological protocols, researching bacterial molecular genetics and data analysis. We're looking for a PhD or equivalent experience in a relevant subject such as microbiology, immunology, genetics or biochemistry. Experience designing protocols and basic microbiological and immunological skills are required.

The deadline for the posts is Noon on 6 June 2018. Both are one year positions. For more details or to apply click here for the Data Scientist role and here for the Molecular Microbiologist role.

The group has moved to the Big Data Institute, University of Oxford

From April we have moved to the Big Data Institute, Nuffield Department of Population Health at the University of Oxford. The group is maintaining its close links to the Modernising Medical Microbiology Consortium and the John Radcliffe Hospital, Oxford. I am grateful to the Robertson Foundation for funding. We're excited about joining new colleagues and benefiting from their expertise in epidemiology, health informatics, genetics and infection, while continuing to cultivate strong links with our existing collaborators in Oxford and around the world.

New paper: Severe infections emerge from commensal bacteria by adaptive evolution

Published this month in eLife, our new paper on the evolution and adaptation of Staphylococcus aureus during infection.

This study shows that the emergence of life-threatening infections of the major pathogen Staphylococcus aureus from bacteria colonizing the nose is associated with repeatable adaptive evolution inside the human body.

First author Bernadette Young has summarized the paper's findings on the Modernising Medical Microbiology blog.

SCOTTI wins PLoS Computational Biology Research Prize

Work from our group has been recognised in the PLoS Computational Biology 2017 Research Prizes. SCOTTI, which infers transmission routes from genetic and epidemiological information, won the Breakthrough in Advance/Innovation category. The citation reads

Our Breakthrough Advance/Innovation winning article presents a new computational tool, called SCOTTI (Structured COalescent Transmission Tree Inference), developed by Nicola De Maio of the University of Oxford (UK), and colleagues. De Maio says, “SCOTTI represents a convenient tool to reconstruct who-infected-whom within outbreaks… [and] has been used in particular for the study of bacterial hospital outbreaks”. It combines epidemiological information about patient exposure with genetic information about the infectious agent itself.

Work is nominated and selected as described in the announcement:

The journal invited the community to nominate their favorite 2016 published Research Articles. From these nominations the PLOS Computational Biology Research Prize Committee, made up of Editorial Board members Dina Schneidman, Nicola Segata, Maricel Kann, Isidore Rigoutsos, Avner Schlessinger, Lilia Iakoucheva, Ilya Ioshikhes, Shi-Jie Chen, and Becca Asquith, selected the winners. To help support future work, the authors of each winning paper will receive award certificates and a $2,000 (USD) prize.

You can read more about SCOTTI and the accompanying paper, written by Nicola De Maio, Jessie Wu and me, here.

Promiscuous bacteria have staying power

An insight article with Ruth Massey on John Lees' and Stephen Bentley's new paper was published in eLife on Friday:

Streptococcus pneumoniae is a notorious bacterial pathogen hiding in plain sight. A common resident of the nose and throat, between 68% and 84% of young infants will carry this species at any given time (Turner et al., 2012). In most cases it causes no harm, yet the presence of pneumococci – as the bacteria are known – can predispose a person to life-threatening infections like pneumonia or meningitis. Indeed, pneumococci are responsible for around 10% of all deaths in young children around the world (O'Brien et al., 2009), with the vast majority of cases being in developing countries.
Research into S. pneumoniae is complicated because the species is a patchwork of distinctive strains and some of these strains remain in the nose and throat for longer than others. Now, in eLife, John Lees and Stephen Bentley – both at the Wellcome Trust Sanger Institute – and colleagues report that strains rendered impotent by a virus do not linger for as long as other strains (Lees et al., 2017).

Click here to read the full piece.

New draft paper on combining p-values through the harmonic mean

In a preprint released today on Biorxiv I report a new method for improving the sensitivity to detect statistical signals by averaging over multiple alternative hypotheses using the harmonic mean p-value. The draft paper looks at example problems in genome-wide association studies (GWAS) in which signals of association may be apparent, but perhaps not sufficiently strong to meet the stringent threshold required to control for the millions of tests performed. Combining weak signals in arbitrary ways - for example across consecutive variants - can reveal signals sufficiently strong to meet the statistical significance threshold. This could be especially useful when looking for interactions, for example between host and pathogen genetics in their effect on infection, because it may be possible to conclude that a particular variant on the host side is involved, even if there is uncertainty over the specific pathogen variant it interacts with. Often such uncertainty arises because of the sheer number of possibilities. Similar ideas are beginning to gain traction in GWAS, and the ability to easily average over hypotheses is one of the strengths of Bayesian statistics. This new paper shows that the benefits of model averaging can be achieved easily in non-Bayesian statistics by taking the harmonic mean p-value from a range of tests. The test is very general and robust to a range of complexities including non-independence between the p-values.

New paper: SCOTTI Efficient reconstruction of transmission within outbreaks with the structured coalescent

New paper published today in PLoS Computational Biology: Understanding how infectious disease spreads and where it originates is essential for devising policies to prevent and limit outbreaks. Whole genome sequencing of pathogens has proved an extremely promising tool for identifying transmission, particularly when combined with classical epidemiological data. Several statistical and computational approaches are available for exploiting genomics for epidemiological investigation. These methods have seen applications to dozens of outbreak studies. However, they have a number of serious drawbacks.

In this new paper Nicola De Maio, Jessie Wu and I introduce SCOTTI, a method for quickly and accurately inferring who-infected- whom from genomic and epidemiological data. SCOTTI addresses very widespread, but generally neglected problems in joint epidemiological and genomic inference, notably the presence of non-sampled and undetected intermediate cases and within-host pathogen variation caused by microevolution. Using real examples and simulations, we show that these problems cause strong misleading effects on existing popular inference methods. SCOTTI is based on BASTA, our recent breakthrough method for phylogeographic inference, and offers new standards of accuracy, calibration, and computational efficiency. SCOTTI is distributed as an open source package within BEAST2.

Prize PhD Studentships available

I am offering two PhD projects as part of the annual Nuffield Department of Medicine Prize Studentship competition:

• Real-time detection of multidrug resistant tuberculosis and transmission in England
  Joint with David Wyllie, molecular microbiologist, this project is focused on developing statistical methods for recognizing transmission clusters, integrating genomics approaches with molecular typing schemes and developing future-proof taxonomy for strain identification.
  
  
• Tracking future infection threats using genomic data and electronic health records
  Joint with David Clifton, biomedical engineer, this project aims to develop new machine learning and statistical methods to identify genomic markers of antibiotic resistance and susceptibility within various pathogens, to help track future infection threats.

These are fully-funded, four-year awards open to outstanding students of any nationality. Applicants nominate three projects, in order of preference, from the available pool. For how to apply, click here. Only applications submitted through the online system will be considered, but interested applicants are welcome to contact me informally. The deadline for applications is noon, 6th January 2017.

In addition to my projects, the Modernising Medical Microbiology project has announced the following PhD projects as part of the competition:

• Antimicrobial resistance gene/vector transmission across human, animal and environmental reservoirs
  Supervised by Nicole Stoesser, Nicola De Maio and Derrick Crook
  
  
• Healthcare big data and genomics for infectious disease threat detection
  Supervised by David Clifton, David Eyre and Tim Peto
  
  
• Prediction of Mycobacterium tuberculosis drug resistance through genome sequencing clinical samples
  Supervised by Tim Walker and Tim Peto
  
  
• Antibiotic resistance in Tuberculosis: Predicting de novo the effect of individual genetic mutations
  Supervised by Phil Fowler and Sarah Walker

The Rsp virulence regulator: new review in Trends in Microbiology

In the September issue of Trends in Microbiology, Mark Smeltzer casts the spotlight on the story of rsp, a virulence regulator in Staphylococcus aureus that evolves within infected patients and may play a role in disease.

The new review covers recent work on the rsp gene including a series papers that my collaborators and my group have contributed: 
Natural mutations in a Staphylococcus aureus virulence regulator attenuate cytotoxicity but permit bacteremia and abscess formation.
Das, S., Lindemann, C., Young, B. C., Muller, J., Österreich, B., Ternette, N., Winkler, A.-C., Paprotka, K., Reinhardt, R., Förstner, K. U., Allen, E., Flaxman, A., Yamaguchi, Y., Rollier, C. S., Van Diemen, P., Blättner, S., Remmele, C. W., Selle, M., Dittrich, M., Müller, T., Vogel, J., Ohlsen, K., Crook, D., Massey, R., Wilson, D. J., Rudel, T., Wyllie, D. H., and M. J. Fraunholz (2016)
Proceedings of the National Academy of Sciences USA 113: E3101–E3110. (abstract pdf)

Evolutionary trade-offs underlie the multi-faceted virulence of Staphylococcus aureus.
Laabei, M., Uhlemann, A.-C., Lowy, F. D., Austin, E. D., Yokoyama, M., Ouadi, K., Feil, E., Thorpe, H. A., Williams, B., Perkins, M., Peacock, S. J., Clarke, S. R., Dordel, J., Holden, M., Votintseva, A. A., Bowden, R., Crook, D. W., Young, B. C., Wilson, D. J., Recker, M. and R. C. Massey (2015)
PLoS Biology 13: e1002229. (abstract pdf)

Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease.
Young, B. C., Golubchik, T., Batty, E. M., Fung, R., Larner-Svennson, H., Votintseva, A., Miller, R. R., Godwin, H., Knox, K., Everitt, R. G., Iqbal, Z., Rimmer, A. J., Cule, M., Ip C. L. C., Didelot, X., Harding, R. M., Donnelly, P. J., Peto, T. E., Crook, D. W., Bowden, R. and D. J. Wilson (2012)
Proceedings of the National Academy of Sciences USA 109: 4550-4555. (abstract pdf F1000)

Lyre bird song

Healesville Sanctuary, Yarra Valley, Victoria, Australia

Tackling and tracking TB through DNA analysis

Collaborative PhD and postdoc positions available

Dr Nicole Stoesser, Prof. Derrick Crook, myself and colleagues in Oxford are seeking a postdoc in Microbial Genomics with statistics skills to join a new three-year project investigating antimicrobial resistance in environmental, human and animal reservoirs of E. coli and related organisms. The application deadline is noon Monday 11th July. For more details click here.

Dr Pierre Mahe of bioMérieux in Grenoble, France, is seeking to appoint an industry-linked PhD position developing statistical methods for genome-based characterization of antimicrobial resistance and virulence genes, with a focus on the opportunistic pathogen Pseudomonas aeruginosa. The position involves a secondment here in Oxford. For more details click here or contact Pierre Mahe.

New paper: How low-toxic Staph. aureus mutants cause severe infections

Published today in PNAS Early Edition, our new paper that reveals naturally occurring mutations in the poorly-described rsp gene of Staph. aureus
reduce toxicity while maintaining the ability to survive, proliferate and cause infection within the human body.

In previous work, we have found that Staph. aureus evolves by mutation within the body quickly enough to influence the progression of disease, and that diversity generated by evolution in the body is a widespread phenomenon. In the case of one patient who we followed longitudinally for over a year, we identified that bacteria in the bloodstream differed from those in the nose by several mutations, of which a loss-of-function mutation in the rsp regulatory gene represented the most likely candidate for playing a possible role in causing severe infection.

We collaborated with Ruth Massey at Bath who discovered to our surprise that while rsp loss-of-function mutants do indeed show differences in toxicity - one of several traditional correlates of virulence readily measured in the laboratory - they showed reduced toxicity. Going further, Ruth and her collaborators showed that bloodstream infections in general show reduced toxicity compared to milder skin infections and asymptomatically carried nose populations, overturning previous views on the relationship between Staph. aureus toxicity and virulence.

Today's new paper offers a detailed dissection of rsp. Working with Claudia Lindemann and David Wyllie at the University of Oxford and Martin Fraunholz and collaborators at the University of Würzburg, we found that although rsp mutants show reduced toxicity, crucially they retain their capacity to survive, grow, spread through the body and cause abscesses. In other words, rsp uncouples toxicity from pathogenicity. This decoupling could be important for evading the immune system and establishing severe infections. To find out more, see the full paper.

Postdoctoral Scientist in Statistical Genomics

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.

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.

CRyPTIC: rapid diagnosis of drug resistance in TB

The Modernising Medical Microbiology consortium has announced a new worldwide collaboration called CRyPTIC to speed up diagnosis of antibiotic resistant tuberculosis (TB).

TB infects nearly 10 million people each year and kills 1.5 million, making it one of the leading causes of death worldwide. Almost half a million people each year develop multidrug-resistant (MDR) TB, which defies common TB treatments. Time consuming tests must be run to identify MDR-TB and which drugs will work or fail. This delays diagnosis and creates uncertainty about the best drugs to prescribe to individual patients.

CRyPTIC aims to hasten the identification of MDR-TB using whole genome sequencing to identify genetic variants that give resistance to particular drugs. The project is funded by a $2.2m grant from the Bill & Melinda Gates Foundation and a £4m grant from the Wellcome Trust and MRC Newton Fund.

CRyPTIC aims to collect and analyse 100,000 TB cases from across the world, providing a database of MDR-TB that will underpin diagnosis using WGS. Samples from across Africa, Asia, Europe and the Americas will be collected by teams at more than a dozen centres They will conduct drug resistance testing and much of the genome sequencing. Read more information here.

Snow Monkeys in Japan

Recently got back from the SMBE Satellite meeting on Pathogen Genomics in Japan. The organizers did a fantastic job and the talks were great. There was also time to visit the Japanese macaques at Snow Monkey Park, where one of the little guys climbed on to my shoulders

Thanks Ashlee Earl for the video and Koji Yahara, Alan McNally and Nick Croucher for additional commentary!

Nature Reviews Microbiology: Within-host evolution of bacterial pathogens

Our new review of what genomics has taught us about Within-host evolution of bacterial pathogens has been published in Nature Reviews Microbiology.

PLoS Biology: Staphylococcus aureus invading the blood are less toxic

Toxicity in nose, blood and skin bacteria.

Collaborative work with Ruth Massey's group at the University of Bath taking forward a study of within-host evolution of Staphylococcus aureus during infection has been published in PLoS Biology. Previously we reported in PNAS that in one patient, bacteria causing a serious bloodstream infection differed by just eight mutations from a persistently carried nose population. We identified one of those mutations as playing a potentially causative role in transforming the nose bacteria into a form capable of bloodstream infection - a regulatory protein called rsp. To investigate further, Ruth applied a number of tests to characterize bacteria taken prior to and during infection. In this new paper, we report the surprising result that the bloodstream isolates show reduced toxicity and that rsp is the responsible for this change.

The notion that isolates responsible for serious human infection are less toxic challenges some long-held beliefs about the mechanism of disease in Staphylococcus aureus infections. Most models of disease assume a straightforward relationship between increased toxicity and greater virulence - the propensity to cause, or severity of, disease.

To test her observation, Ruth collaborated with groups from New York and Cambridge to investigate whether the pattern observed in one patient held more generally across 134 Staphylococcus aureus belonging to the notorious USA300 strain. It did.

Curiously, bacteria isolated from the skin and from superficial infections were equally toxic to nose bacteria. These findings raise new questions about the role of toxicity in colonization, transmission and serious infections of Staphylococcus aureus. One possibility that we wish to investigate further is whether toxicity might be required for the usual transmission of Staphylococcus aureus populations in the nose, skin or superficial infections (such as impetigo), whereas loss of toxicity may promote transition to deep tissue and bloodstream infections by evading immune defences.

New paper: Rapid host switching in Campylobacter

Our new open access paper Rapid host switching in generalist Campylobacter strains erodes the signal for tracing human infections was published last week in the ISME Journal.

 

With Bethany Dearlove, Sam Sheppard and colleagues, we investigated common strains of campylobacter, the most frequent cause of bacterial gastroenteritis worldwide. Campylobacter infection is associated with food poisoning, particularly contaminated chicken. But in previous work, we found that certain strains (the ST-21, ST-45 and ST-828 complexes) are often found contaminating a range of meat and poultry, making it difficult to trace the source of human infection.

That previous work was based on partial genome sequencing known as MLST. In MLST, less than 1% of the information in the genome is captured. Now that whole genome sequencing is available, the expectation was that we should be able to distinguish easily between between ST-21, 45 and 828 strains contaminating poultry versus beef versus lamb, and so on.

What we found was surprising. Instead of these strains harbouring previously unobserved sub-structure that allowed them to be associated with different animal sources, we found rapidly mixing populations undergoing extremely fast transmission between animal species, with campylobacter strains ricocheting among animal species on a timescale of just a few years. This is faster than they can accumulate enough mutations to differentiate populations colonizing different animal species.

Our results present an unforeseen roadblock to tracing transmission with whole genome sequencing, and suggests these strains are adapted to a generalist lifestyle, shedding new light on the ecology of this pathogen. These findings push back against the tide of opinion that whole genome sequencing is necessarily a panacea for detecting transmission, and demonstrate that going forwards, a detailed understanding of the biology of zoonotic bacteria (those transmitting between multiple species) and intensive sampling of potential sources are essential for effectively tracing the source of human infection.

BASTA: Improved method for phylogeography

This week sees publication of our paper New Routes to Phylogeography: a Bayesian Structured Coalescent Approximation in PLoS Genetics.

Phylogeography is the recovery of migration history from genome sequences, and has exploded as a field in recent years. Over a thousand papers have used contemporary sequences and ancient DNA to reconstruct migratory trends, locate the origin of outbreaks and track the spread of infectious diseases. In many high profile examples phylogeography has informed our understanding of how major human pathogens spread.

In our new paper we solve a severe and apparently widely unappreciated problem: that the most popular approaches to phylogeography are heavily biased, extremely sensitive to sampling structure and substantially underestimate statistical uncertainty. The problems stem from the treatment of migration as equivalent to mutation (discrete trait analysis; DTA), and the assumption that sampling locations are phylogeographically informative.

To solve these problems we introduce and demonstrate a new method BASTA, implemented in the phylogenetic software package BEAST2, that employs a novel approximation to enable inference under the structured coalescent – the bottom-up population genetics model of migration. Previously, methods for exact inference under the structured coalescent have proven too slow for many practical purposes, hence the need for a fast and accurate approximation.

The biases we highlight with popular phylogeography methods are much more important than might appear from what is at one level a question of model choice. To underline this, we present an analysis of around 100 Ebola virus genome sequences to investigate the emergence of human outbreaks. Epidemiological studies have found that animals act as a reservoir, maintaining the virus between the sporadic human outbreaks that have unfolded over the past four decades, a scenario that our structured coalescent-based model correctly identifies.

Remarkably, DTA, the de facto standard method for phylogeography, wrongly concluded with high confidence that Ebola has been maintained since 1976 by undetected human-to-human transmission between outbreaks. Although such a conclusion would never be believed in the case of Ebola, it makes clear the potential for highly misleading inference about transmission that could, for much less well understood diseases, have serious implications for public health policy.

BASTA is the result of a lot of hard work by Nicola De Maio, who is a James Martin Fellow at the Oxford Martin School Institute for Emerging Infections, with help from Jessie Wu and Kathleen O'Reilly. You can read the paper here and download BASTA here.