The Molecular Ecologist: Mutation rates shaped by population dynamics

Polio virus (picornavirus) Photo by Sanofi Pasteur.

Over at The Molecular Ecologist, I have a new post up discussing an interesting new modeling paper. It suggests that, for some viruses, variation in the rate of evolutionary change may be driven not by selection imposed by their hosts, but by the dynamics of the viral population within, and spreading among, host individuals.

Viruses based on RNA, as opposed to DNA, generally have very high mutation rates—in part because the process of replicating RNA is more error-prone than DNA replication. But there’s also tremendous variation in the substitution rate between different RNA viruses, even between populations of closely related viruses.

To find out how simple population dynamics could shape this wide variation in substitution rates, go read the whole thing.◼

Baby steps versus long jumps: The “size” of evolutionary change, and why it matters

Evolution can make leaps—but how frequently? Photo by Flavio Martins.

ResearchBlogging.orgDoes evolutionary change happen in big jumps, or a series of small steps? The question may seem a little esoteric to non-scientists—how many mutations can dance on the head of a pin?—but it has direct implications for how we identify the genetic basis of human diseases, or desirable traits in domestic plants and animals.

That’s because the evolutionary path by which a particular phenotype, or visible trait, first evolved in a population is closely related to the genetics that underlie the trait in the present. Phenotypes that arose in a single mutational jump will probably remain connected to one or a few genes with large effects; phenotypes that evolved more gradually do so because they are created by the collective action of many genes. So what kind of evolutionary change is most common will determine which kind of gene-to-phenotype relationships we should expect to find.

In an excellent recent review article for the journal Evolution, Matthew Rockman, a biologist with the Department of Biology and Center for Genomics and Systems Biology at New York University, makes the case that the era of genomics has, so far, been much too focused on finding genes of large effect. Fortunately, Rockman also sees the beginnings of a new movement towards acknowledging the importance of small-effect genes—one which may ultimately make genomic association studies more useful.

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When does a beneficial mutation fail to benefit?

Beneficial mutations, according to Hollywood, include the superpowered ability to make San Francisco Bay foggy. Photo via Comics Contiuum.

ResearchBlogging.orgEvery time a cell divides is an opportunity for mutation, creating new genetic variation that may be beneficial, may be harmful, or may make no difference at all. In sexually reproducing species, the fate of a useful new mutation is relatively straightforward. If it overcomes the vicissitudes of genetic drift, the mutation will spread through the population as recombination swaps it into different genetic backgrounds, so that on average the mutation spreads or disappears on its own merits.

In asexual species, though, things are less straightforward. This is because new mutations are stuck with the genetic backgrounds in which they first appear—whether they spread of disappear depends not only on the fitness benefits they might provide, but on how beneficial the variation in the rest of the genome is, too. A new beneficial mutation in an asexual population is like a race car driver who can’t change cars—she might be an ace at the wheel, but if she’s stuck in a Yugo, she’s probably not going to win.

So what happens to a new beneficial mutation in an asexual population is largely dependent on random factors: genetic drift and mutation. That randomness means that in order to know how new useful mutations behave in general, the only robust solution is to watch lots of new useful mutations in lots of otherwise identical populations.

In other words, it’s a question best approached using experimental evolution. That brings us to a study just released in advance of print by the journal Genetics, in which a team headed by Greg Lang uses some clever methods to track the origin and fate of beneficial mutations in yeast.

The first clever thing about the project is that its authors knew in advance where to expect a beneficial mutation. Yeast cells reproduce both sexually and asexually—if the experimental populations are maintained in conditions that keep them reproducing asexually, mutations that turn off the costly cellular machinery necessary for sexual reproduction provide a measurable benefit.

Electron micrograph of budding yeast cells. Image from Microbe World.

By using a strain of yeast engineered to produce fluorescent protein in the course of sexual reproduction, the authors could check for the presence of permanently asexual mutants by taking a sample from the population, prompting it to mate and measuring the sample’s total fluorescence. Lower fluorescence would mean that more cells had lost the ability to reproduce sexually; if samples from a population were to become less and less fluorescent over time, the beneficial mutation would be spreading through the population.

Lang and his coauthors then set up the kind of experiment that you can only do with single-celled critters: they started 592 populations of yeast evolve for 1,000 generations of asexual reproduction. Each population started out from a single genetic strain, so differences between populations started from the same strain were purely due to differences in the random processes of mutation and drift. (The full experimental design used two different strains of yeast, and kept the population size at either 100,000 or 1,000,000 cells, for a total of four treatments.)

You might expect that the loss-of-sex mutation would reliably emerge and spread until it dominated each replicate population. In fact, that only occurred in a small fraction of the replicates. In many more cases, the loss-of-sex mutation showed up and started to spread, but was then overwhelmed by yeast that could still reproduce sexually—presumably because other, more beneficial mutations had arisen elsewhere in the population. This phenomenon, clonal interference, is widely expected to happen in competition among clonal strains.

What determined the success or failure of the loss-of-sex mutation? The authors found a considerable range of variation in the rate at which loss-of-sex strains increased in the experimental populations, suggesting that variation elsewhere in the genome contributed to the fitness of the yeast strain carrying the loss-of-sex mutation. Since every replicate population started as a genetically identical clone, that meant that mutations built up quite early in the course of experimental evolution. That variation corresponded to differences in the fitness of strains within the population—and the success or failure of the loss-of-sex mutation depended on whether it turned up in a strain that was already pretty fit to begin with.

Without recombination to mix up the genome, a beneficial mutation is bound to genetic variants at many, many other loci that may boost the benefits from that mutation, or cancel them out. In a clonal population, each genome succeeds or fails as a unit—a single useful mutation simply cannot do it alone.


Lang, G., Botstein, D., & Desai, M. (2011). Genetic variation and the fate of beneficial mutations in asexual populations. Genetics DOI: 10.1534/genetics.111.128942

Lang, G., Murray, A., & Botstein, D. (2009). The cost of gene expression underlies a fitness trade-off in yeast. Proc. Nat. Acad. Sciences USA, 106 (14), 5755-60 DOI: 10.1073/pnas.0901620106

The “Big Four,” part II: Mutation

This post is the second in a special series about four fundamental forces in evolution: natural selection, mutation, genetic drift, and migration.

This post was chosen as an Editor's Selection for ResearchBlogging.orgIn order for populations to change over time, to descend with modification, as Darwin originally put it, something has to create the modifications. That something is mutation.

A mutation of large effect? Photo by Cayusa.

A mutation is any change to an individual’s genetic code, whether caused by an external factor like radiation, or an error in the DNA copying that takes place every time an individual cell divides. However, not all mutations are created equal.

First and foremost, for a mutation to have any future existence beyond the individual in which it occurs, it must be in a cell that will go towards forming the next generation, a germline cell. In sexually reproducing species, this means sperm or egg cells, or the progenitor cells in the testes or ovaries that form them. Second, for a mutation to be “visible” to natural selection, it must have some effect on fitness, the number of offspring an individual carrying the mutation is likely to have. The genetic code determines how and when individual cells make proteins, and proteins determine phenotypes, the visible characteristics of living things. However, many changes to the genetic code don’t affect their carriers’ fitness. Mutations might be more or less neutral because

  • They occur in regions of the genome that don’t code for proteins or control protein production—so-called “junk” DNA.
  • They are synonymous substitutions, which occur in a protein-coding region of DNA, but don’t alter the protein produced.
  • They alter a protein, but not in a way that changes its function [PDF].
  • They alter a protein’s function and an individual’s phenotype, but in a way that doesn’t affect how many offspring that individual has.

Neutral mutations are actually quite important for biological studies—DNA fingerprinting and population genetics studies rely on them. Their frequencies evolve at random, reflecting the history of the populations that carry them rather than the effects of natural selection.

Favorable new mutations sweep the population

TOP: DNA sequences from a population are variable (blue) before a new mutation (red dot) arises; after it “sweeps,” every individual carries the allele as well as identical sequence nearby (red). BOTTOM: a figure from Linnen et al. (2009), demonstrates this pattern in deer mice. Images from Pritchard et al. (2010), fig. 3 and Linnen et al. (2009), fig. 4.

The variation introduced by neutral mutations—or, rather its absence—can help identify regions of the genome where selection is active. When a new gene arises by mutation, and it is strongly favored by selection, it can quickly spread through a population. In species that remix their genomes through sexual selection, the region of the genome containing a useful new gene can recombine into many different genetic backgrounds—but the closer a region of DNA is to the favored mutation, the less likely it is to recombine and separate from it. Thus, a region of the genome rather larger than the gene favored by selection is carried along until everyone in the population has the same DNA sequence.

When a new mutation takes over a population in this manner, it’s called a selective sweep, and the pattern it produces has been used to identify genes recently favored by selection in many different species, including humans. For instance, Linnen et al. documented reduced genetic variation in the neighborhood of gene variant responsible for light-colored fur in deer mice to demonstrate that it spread rapidly through the population after the mice colonized a region with light-colored sand.

Fuel for the engine of natural selection

Selective sweeps highlight how natural selection acts in opposition to mutation: mutation introduces new variation into populations, and natural selection causes the most fit variants to spread—potentially until the whole population carries the same trait. At the same time, selection requires variation in order to operate. If everyone is identical, then everyone has the same expected number of offspring, and the next generation will look just like the current one.

Because of this, natural selection can only operate as rapidly as mutation can introduce new variation from which to select. We know of specific cases in which selection seems to have “stalled” for lack of heritable variation. For instance, the fly Drosophila birchii lives in rainforest habitats along the northeast coast of Australia. Fly populations from the driest locations in this range have greater tolerance for dry conditions, but they also have virtually no heritable variation for drought tolerance [PDF]—and the authors suggest that this could limit the flies’ ability to evolve in response to climate change.

In other cases, though, biologists have found that mutation seems to provide new variation at least as fast as selection can remove it, leading to sustained, long-term evolution of experimental populations [PDF]. One important factor that may determine the outcome of this mutation-selection balancing act is actually the size of the population—more individuals means more opportunities for mutations to occur.

So the rate at which new mutations accumulate in a population depends on many factors, not the least of which are how you choose to measure that rate, and the fitness effects of the counted mutations. (Does a mutation “count” as soon as it occurs in a cell’s nucleus, or only when it has passed on to the next generation, or only when it has spread to everyone in a population?) Ultimately, populations evolve through a constant tension between the effects of mutation, natural selection, and the subject of next week’s Big Four force: genetic drift.


Barton, N., & Keightley, P. (2002). Understanding quantitative genetic variation. Nature Reviews Genetics, 3 (1), 11-21 DOI: 10.1038/nrg700

Drake J.W., Charlesworth B., Charlesworth D., & Crow J.F. (1998). Rates of spontaneous mutation. Genetics, 148 (4), 1667-86 PMID: 9560386

García-Dorado, A., Ávila, V., Sánchez-Molano, E., Manrique, A., & López-Fanjul, C. (2007). The build up of mutation-selection-drift balance in laboratory Drosophila populations. Evolution, 61 (3), 653-65 DOI: 10.1111/j.1558-5646.2007.00052.x

Hoffmann, A., Hallas R.J., Dean J.A., & Schiffer M. (2003). Low potential for climatic stress adaptation in a rainforest Drosophila species. Science, 301 (5629), 100-2 DOI: 10.1126/science.1084296

Keightly, PD. (2003). Mutational variation and long-term selection response. Pages 227-48 in Plant Breeding Reviews, Volume 24, part I. J. Janick, ed. John Wiley & Sons. Google Books.

Linnen, C., Kingsley, E., Jensen, J., & Hoekstra, H. (2009). On the origin and spread of an adaptive allele in deer mice. Science, 325 (5944), 1095-8 DOI: 10.1126/science.1175826

Pritchard, J., Pickrell, J., & Coop, G. (2010). The genetics of human adaptation: Hard sweeps, soft sweeps, and polygenic adaptation. Current Biology, 20 (4) DOI: 10.1016/j.cub.2009.11.055

Tokuriki, N., & Tawfik, D. (2009). Protein dynamism and evolvability. Science, 324 (5924), 203-7 DOI: 10.1126/science.1169375

Back to basics: The “Big Four”

ResearchBlogging.orgThe nice thing about a field season away from all regular internet access is that it gives you a real sabbatical of a sort—a chance to reassess plans and set new goals. One of the new goals I set myself this last field season was to introduce a new kind of topic here at Denim and Tweed.

Most of my writing about science at D&T focuses on recently published discoveries in evolution and ecology. It’s fun writing, and it coincides neatly with my regular journal reading, and I intend to keep doing it. But I’ve discovered that when I want to put new work in context, I often need to discuss fundamental concepts of evolutionary biology that aren’t necessarily common knowledge, such as genetic drift or sexual selection. However, I rarely have room to explain these concepts in depth within a blog post devoted to something else.

So maybe the solution is to devote some posts to explaining these “basics.” I’m going to start with a series of posts on the “Big Four” processes of population genetics. These are the four processes that account, in one way or another, for every change in the frequency of genes within natural populations. In other words, the Big Four account for much of evolution itself. They are:

  • Natural selection, changes in gene frequencies due to fitness advantages, or disadvantages, associated with different genes.
  • Mutation, the source of new forms of genes;
  • Genetic drift, or changes in gene frequencies that arise from the way probability works in finite populations; and
  • Migration, or changes in gene frequencies due to the movement of organisms from site to site.

Lay readers may be surprised both by what we know, and what we don’t, about how these four processes operate in nature. Natural selection is relatively easy to measure, and apparently ubiquitous [PDF] in natural populations—but we don’t know how often the resulting short-term changes impact evolution over millions of years. Mutation, the source of variation on which natural selection acts, seems to vary widely among living things. Genetic drift means that a trait can come to dominate a population even if it has no fitness effect—or sometimes a deleterious one. Finally, migration across variable landscapes can interact with selection, drift, and mutation [$a] to completely alter their effects.

I’ll devote one post each to selection, mutation, drift, and migration, discussing classic findings as well as more recent scientific discoveries about each. They’ll arrive as my usual mid-week science posts for the next four weeks, and I’ll update this post with links to the others as they go online—so if this looks worth following, you can either bookmark this post, or subscribe to D&T’s RSS Feed.

Natural selection, mutation, genetic drift, and migration act together to shape the evolution of natural populations. Photo by jby.


Drake JW, Charlesworth B, Charlesworth D, & Crow JF (1998). Rates of spontaneous mutation. Genetics, 148 (4), 1667-86 PMID: 9560386

Kingsolver, J., Hoekstra, H., Hoekstra, J., Berrigan, D., Vignieri, S., Hill, C., Hoang, A., Gibert, P., & Beerli, P. (2001). The strength of phenotypic selection in natural populations. The American Naturalist, 157 (3), 245-61 DOI: 10.1086/319193

Slatkin, M. (1987). Gene flow and the geographic structure of natural populations. Science, 236 (4803), 787-92 DOI: 10.1126/science.3576198

Wright S (1931). Evolution in Mendelian populations. Genetics, 16 (2), 97-159 PMID: 17246615