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.

Neolithic origin of Campylobacter jejuni

As part of a recent trip to the University of Edinburgh to visit Andrew Rambaut, I gave a talk on some work of mine published in the February edition of Molecular Biology and Evolution and subsequently recommended on the Faculty of 1000 website about the evolution of the gut pathogen Campylobacter jejuni.

Part of the paper is concerned with the issue of the timescale of Campylobacter evolution, and using longitudinal samples of C. jejuni DNA sequences we attempted to calibrate the molecular clock in a similar way to that which is standard practice for viruses.

We detected surprisingly rapid evolution - 1,000 times faster than traditional estimates - which would place the split of C. jejuni from its closest relative C. coli during the Neolithic revolution. Interestingly, the point estimate of 6,500 years ago for the split from C. coli - which preferentially infects swine - coincides with the spread of pig domestication in the Near East and Europe in the 4th millennium BC.

The date is controversial because the traditional dating method, which is based on bounding deep phylogenetic splits such as the common ancestor of mitochondria and bacteria, would place the divergence of C. jejuni and C. coli closer to 10 million years ago.

After the seminar I had an interesting discussion with Paul Sharp, who was in the audience. Prof Sharp is actively researching the causes of conflict between long-term and short-term estimates of the rate of evolution in viruses. As he points out, short-term rate estimates (usually based on longitudinally-sampled viral sequences) frequently suggest that evolution is occurring much more rapidly than long-term estimates (based on deeper calibration points, such as co-phylogeny of host and pathogen). This phenomenon, observed in HIV and hepatitis C among others, may be caused by overly simplistic models of sequence evolution.

So how plausible is it that a ubiquitous bacterial pathogen such as C. jejuni evolved as recently as the Neolithic, possibly in response to changes brought about by agriculture or animal husbandry? Longitudinal studies of Helicobacter pylori and Neisseria gonnorhoeae have obtained similarly rapid rates of bacterial evolution, and evidence is mounting that the Neolithic revolution played an important role in creating new niches for human, plant and animal pathogens. Perhaps the best prospect for resolving these questions will be studies of ancient DNA preserved from the period in question.