Many genes, but two major roads to adaptation

Fruit fly (Drosophila melanogaster, male) Drosophila melanogaster. Photo by Max xx.

Cross-posted at Nothing in Biology Makes Sense!

In the course of adaptive evolution — evolutionary change via natural selection — gene variants that increase the odds of survival and reproduction become more common in a population as a whole. When we’re only talking about a single gene variant with a strong beneficial effect, that makes for a pretty simple picture: the beneficial variant becomes more and more common with each generation, until everyone in the population carries it, and it’s “fixed.” But when many genes are involved in adaptation, the picture isn’t so simple.

This is because the more genes there are contributing to a trait, the more the trait behaves like a quantitative, not a Mendelian, feature. That is, instead of being a simple question of whether or not an individual has the more useful variant, or allele, at a single gene — like a light switch turned on or off — it becomes possible to add up to the same trait value with different combinations of variants at completely different genes. As a result, advantageous alleles may never become completely fixed in the course of an adaptive evolutionary response to, say, changing environmental conditions.

That principle is uniquely well illustrated by a paper published in the most recent issue of Molecular Ecology, which pairs classic experimental evolution of the fruitfly Drosophila melanogaster with modern high-throughput sequencing to directly observe changes in gene variant frequencies during the course of adaptive evolution. It clearly demonstrates that when many genes contribute to adaptation, fixation is no longer inevitable, or even necessary.

Turning up the heat, homogenizing flies

The authors of the new study, a team from the Institut für Populationsgenetik led by Pablo Orozco-terWengel, conducted what would otherwise be a rather simple experiment in evolutionary change in the laboratory. Starting with fruitflies collected from a wild population in Portugal (yes, Virginia, Drosophila melanogaster has wild populations!) they established three replicate populations of about 1,000 flies, which they put in temperature-controlled conditions somewhat warmer than the original collection location, and allowed them to propagate for 37 generations. Exensive previous work with Drosophila has established that simply moving the flies into a laboratory setting — where they live in bottles, and eat prepared food — exerts natural selection on them, and the increased temperature added a little bit more novelty to the lab environment to make it more likely adaptation would occur.

This experiment is different from all that previous experimental evolution of Drosophila, though, is that the coauthors tracked allele frequencies at thousands of markers during the course of those 37 generations of adaptation to the lab. To do this efficiently, they used an approach called “pooled sequencing.”

The principle behind pooled sequencing is that, if all you care about is the relative frequency of a gene variant in a whole population, you don’t need to know the genotype of any specific individual in that population. So to track changes in allele frequency, the team sampled hundreds of flies from the experimental population, and ground them all up together. (The polite, technical term used here is “homogenized.”) They then extracted DNA from this “pooled” sample, and used a high-throughput sequencer to collect millions of reads — short snippets of DNA sequence — out of the pool as a whole.

To extract allele frequencies from all of those sequence reads, the team identified where each read matched the Drosophila melanogaster reference genome. When multiple reads matched to the same location, but differed in one or more DNA nucleotide bases, they identified those bases as variable markers — single-nucleotide polymorphisms, or SNPs. Because the original DNA sample was pooled from many mashed-together flies, the relative frequency of each different variant of a SNP in the Illumina output should reflect the relative frequency of that SNP variant in the population as a whole.

Using this approach, Orozco-terWengel et al. could track allele frequency changes across more than a million SNP markers by taking these pooled samples from the intial population of flies, then at multiple points during the 37-generation evolutionary experiment. By comparing the allele frequencies in samples taken during the course of adaptation to the allele frequencies in the sample from the starting population, they could identify SNPs that became more common as the population adapted — and, because they had a big sample from across the genome, they could identify those SNPs whose allele frequencies had changed more than would be expected due to genetic drift. They examined samples taken after 15 and 27 generations of evolution, and at the end of the 37-generation experiment.

Two paths to adaptation

Fruit fly (Drosophila melanogaster, male) Allele frequency changes (AFC) in SNPs showing significant change by generation 15 (a) and by generation 37 (b). Image from Orozco-terWengel et al. (2012), figure 3.

What they found was largely in line with the verbal model I outlined at the beginning of this post. Over the course of experimental evolution, significant increases in allele frequency occurred at thousands of SNPs — suggesting that a great many genes are involved in the process of adaptation to life in the lab. Accordingly, very few of those allele frequency changes (in about 0.5% of the 2,000 SNPs that showed the greatest change from start to finish) represented complete or near-complete fixation.

More interestingly, comparison of allele frequency changes at the 15th generation and at the end of the experiment revealed two major “paths” taken by alleles. In the first case, the SNPs with strongest allele frequency changes by generation 15 all hit a “plateau” in subsequent generations — they didn’t see any significant increase in frequency between generations 15 and 37. In the second case, SNPs with the strongest allele frequency changes by generation 37, the end of the experiment, had increased steadily from the beginning population through the samples taken at the 15th and 27th generation. The SNPs in this second set had not shown significant allele frequency increases by generation 15 — which means the SNPs underlying most of the adaptive change in the first half of the experiment were a completely different set than the SNPs underlying adaptive change in subsequent generations.

If it’s already adapted, don’t fix it.

On the one hand, that suggests that Orozco-terWengel et al. managed to capture SNPs with a range of different contributions to the adaptation the observed by the end of the experiment. The SNPs with the biggest contribution showed rapid initial increases in allele frequency, then leveled off; SNPs with weaker effects showed slower, steady increases that continued for the entire experiment. But if it’s that simple, why didn’t the large-effect SNPs show continuing allele frequency change after the midpoint of the experiment?

It may be, as the coauthors speculate, that the two classes of SNPs identified in their experiment are separated by more than just the size of their respective contributions to adaptive change. There could be interactions among the alleles at these SNPs, such as overdominance, in which an individual is most fit when he or she carries two different alleles at a locus, rather than two copies of either allele. Overdominance would explain why most of the SNPs showing rapid initial increases in allele frequency then leveled out at intermediate frequencies.

So this combination of experimental evolution and modern sequencing technology raises some interesting questions even as it supports a lot of previous thinking about how natural selection acts on traits that are created by the collective action of many genes. It’s an exciting result, and, I hope, inspiration for much more work digging into the details of such “polygenic” adaptation.◼


Burke, M. and A. Long. 2012. What paths do advantageous alleles take during short-term evolutionary change? Molecular Ecology 4913–4916. DOI: 10.1111/j.1365-294X.2012.05745.x.

Orozco-Terwengel, P., M. Kapun, V. Nolte, R. Kofler, T. Flatt and C. Schlötterer. 2012. Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Molecular Ecology 4931–4941. 10.1111/j.1365-294X.2012.05673.x.

Pavlidis, P., D. Metzler and W. Stephan. 2012. Selective sweeps in multi-locus models of quantitative traits. Genetics 192:225–239. DOI: 10.1534/genetics.112.142547.