Missing the forest for the trees: How frequent adaptation can confound its own inference

Blog author: Fabian Staubach was a postdoc in Dmitri Petrov’s lab and is now an assistant professor in Freiburg, Germany.

This post was written by Fabian Staubach. 

The neutral theory of molecular evolution assumes that adaptation is rare and that the effect of adaptation on linked variation, the so-called hitchhiking effect, typically has only little influence on the dynamics of molecular genetic variation. Because of this assumption, it is widely assumed that in most natural populations, hitchhiking can be neglected, or at least reasonably well approximated by the introduction of effective parameters, such as an effective population size. But if molecular adaptation is in fact common, then the assumption may be violated, and we should worry whether population genetic methods and estimates of evolutionary parameters obtained from them are robust to frequent hitchhiking.

In their paper “Frequent adaptation and the McDonald-Kreitman test” (PNAS, 2013), Philipp Messer and Dmitri Petrov investigate this question for one of the key population genetic methods — the McDonald-Kreitman (MK) test. This test forms the basis of most commonly used approaches to measure the rate of adaptation from population genomic data and has been used to argue that in some organisms, such as Drosophila, the rate of adaptation is surprisingly high.

The MK test can substantially underestimate the true rate of adaptation

Messer and Petrov employ their powerful forward simulation software, SLiM (see here), to simulate the evolution of entire chromosomes under a range of parameter values relevant to humans and other organisms, and apply various forms of the MK test to the population genomic data resulting from their simulations. They then study how accurately these methods re-infer the true evolutionary parameters in the simulations. Strikingly, they find that the MK test can substantially underestimate the true rate of adaptation. For instance, they present scenarios where 40% of the amino acid changing substitutions were in fact strongly adaptive in the simulations, while other population parameters resembled those commonly inferred for human evolution, yet the standard MK estimates yield that none of these substitutions were actually adaptive. Fortunately, Messer and Petrov propose a way to avoid these problems by using a simple, asymptotic extension of the MK test.

Figure: Illustration of the asymptotic MK estimation of the rate of adaptive substitutions : The standard MK approach assumes that all polymorphisms (non-synonymous and synonymous) are neutral. This assumption is likely violated for low frequency polymorphisms, as some of these are likely to be deleterious. The assumption should hold for very high frequency polymorphisms, because they are very unlikely to be deleterious. The asymptotic MK approach uses this fact by looking at the estimated rate from different frequency classes of alleles, and extrapolating to x=1, where the rate is expected to have asymptoted.  

The bigger claim of this straightforward and easy-to-read paper is that the effects of linked selection cannot be simply swept under the rug by introducing effective parameters, such as effective population size or effective strength of selection, and then using these effective parameters in formulae derived from the diffusion approximation under the assumption of free recombination.

Quantifying known biases

Surely, this paper will ruffle some feathers. Some people will argue that these problems have been know for a while in theory. Yet despite this, the vast majority of studies that continue to appear in the literature still pay only cursory lip service, if anything, to these issues. Presumably, this is because it is not well understood analytically to what extent linkage effects affect population genetic estimates, and Messer and Petrov therefore do an important job in quantifying these biases. Hopefully this will help focus the community’s attention to spend some time figuring out how to modify commonly used approaches to place them on a more solid foundation.

Citation: Messer, P. W., & Petrov, D. A. (2013). Frequent adaptation and the McDonald-Kreitman test. Proceedings of the National Academy of Sciences of the United States of America, 110(21), 8615–20. doi:10.1073/pnas.1220835110

Paper author: Philipp Messer is a research associate in Dmitri Petrov’s lab at Stanford, where he studies the population genetics of adaptation using theoretical and computational approaches in concert with the analysis of large-scale population genomic data.

The fruit fly and its microbiome

Philipp Messer is a research associate in the Petrov lab

This post was written by Philipp Messer.

Although fruit flies are one of the most important model organisms in genetics, evolution, and immunology, surprisingly little is known about their associated microorganisms (their microbiome). This is even the more surprising if you consider that the microbiome can strongly affect quantitative traits in flies, for example their growth rate and cold tolerance. Furthermore, the natural environment of fruit flies – rotting fruit – is very rich in microorganisms.

All organisms interact with associated microbes

Because microbes can influence the phenotype of organisms, we expect such interactions to be subject to natural selection. Genes involved in pathogen defense are indeed amongst the fastest evolving genes. But interactions with microbes do not always just lead to an evolutionary arms race between microbes and their hosts, they can also facilitate major evolutionary innovations. Prominent examples of such innovations are the light organ of the bobtail squid that arose through a symbiotic relationship between squids and bioluminescent bacteria, or cellulose digestion in termites which relies on microbes in their guts. Hence, to improve our understanding of the evolution of fruit flies, we need to better understand how they interact and coevolve with their associated microorganisms.

In their paper “Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment”, Fabian Staubach and his colleagues at Stanford and the Max Planck Institute for Evolutionary Biology in Plön shed light on some of the major questions regarding Drosophila associated microbes. Beyond finding out which bacteria are present in flies, they assess the relative roles of host species and environmental effects on bacterial communities, detect candidate natural pathogens, and find interesting results regarding lab-of-origin-effects on the fly microbial community.

We need more studies like this

These results are not only highly relevant for everyone working with Drosophila, but are also a strong reminder that we cannot understand any model organism without taking its associated microbiota into account. We therefore need more microbiome studies like that of Staubach et al to identify the microbes that coevolve with their hosts and understand how the genomes of hosts and microbes interact in the evolutionary process. I would not be surprised if interactions between microbes and their hosts turn out to be among the biggest selective forces in many organisms.

The paper is a fun and easy read and can be found at here. Fabian was a postdoc in the Petrov lab from 2010 to 2013 and has just moved to the University of Freiburg in Germany to start his own group, where he plans to follow his interest to deepen our understanding of the role of microbes in adaptation.

Citation: Staubach F, Baines JF, Künzel S, Bik EM, Petrov DA (2013) Host Species and Environmental Effects on Bacterial Communities Associated with Drosophila in the Laboratory and in the Natural Environment. PLoS ONE 8(8): e70749. doi:10.1371/journal.pone.0070749

Fabian Staubach studies the microbiome of fruitflies.